Module 3 – Case DISTRIBUTION & MARKETING PLAN ASSIGNMENT OVERVIEW Online shopping has increased dramatically over the past few years, and online retailing becomes the top priority of all major grocery retailers. On one hand, retailers see the needs to off

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Module 3 – Case

DISTRIBUTION & MARKETING PLAN

ASSIGNMENT OVERVIEW

Online shopping has increased dramatically over the past few years, and online retailing becomes the top priority of all major grocery retailers. On one hand, retailers see the needs to offer easy and convenient shopping venues to attract and keep customers. On the other hand, the consumers take advantage of the extended product assortments and services offered by both online and traditional retailers. More and more traditional brick-and-mortar retailers such as Best Buy, Walmart, Costco, and Macy’s are becoming brick and click stores. Multichannel retailing is popular way to sell products and services nowadays. The marketing managers need to find ways to effectively and efficiently manage various distribution channels and deliver products and service through both online and offline channels. In this case, we focus on Macy’s brick-and-click channel-management issues.

CASE READING ( SEE ATTACHED PDF)

Review the followings articles related to brick and click shopping and multichannel retailing { (Use online library search engine and additional library resources on TLC Portal to find the articles) see attached below}:

Avery, J., Steenburgh, T.J., Deighton, J., & Caravella, M. (2013). Adding bricks to clicks: On the role of physical stores in a world of online shopping. GfK Marketing Intelligence Review, 5(2), 29.

Birchall, J. (2009, May 14). Macy’s online sales rise 16% despite store woes. The Financial Times, pp. 16.

Clicks and bricks; retailers and the internet. (2012). The Economist, 402 (8773), 18.

Griffiths, G H & Howard, A (2008). Balancing clicks and bricks – strategies for multichannel retailers. Journal of Global Business Issues, 2(1), 69.

Katia Campo, & Els Breugelmans. (2015). Buying groceries in brick and click stores: Category allocation decisions and the moderating effect of online buying experience. Journal of Interactive Marketing, 31, 63.

Macy – online order fulfillment center expansion – Arizona. (2014). World Market Intelligence News

Macy’s fights online shopping with a tablet in fitting rooms. (2015). Engadget [Engadget – BLOG]

https://www.engadget.com/2015/08/18/macys-tablets-…

Mixing bricks with clicks; retail. (2013). The Economist,406(8828), 70.

Newman, D. (2016). The Top 10 Trends Driving Marketing In 2017. Forbes. Retrieved from

http://www.forbes.com/sites/danielnewman/2016/10/1…

CASE ASSIGNMENT

Develop a report in terms of the following guidelines based on the articles listed above and other supplemental articles. At least two additional articles should be included in the analysis. A well-written report should have a brief introduction, headings or subheadings, and a brief concluding comment. Note that you should use some keywords as headings or subheadings such as “Facts Recap,” instead of a sentence or a question.

Briefly review the facts on the brick and click retailing and multichannel shopping, as reported in the articles.
Describe how Macy’s performed in online channel? How Macy’s could do a better job to manage the online and offline channels? Consider appropriate distribution concepts to support your arguments.
Considering the changing consumer shopping behaviors and new technology trends, how would you recommend Macy’s compete with Amazon and other retailers in the future?

ASSIGNMENT EXPECTATIONS REGARDING YOUR REFERENCES AND DEFENSE OF YOUR POSITIONS

Write clearly, simply and logically. Your paper should be 750-1,000 words long, excluding title pages and references, but quality of writing is more important than length. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.

Back up your positions or opinions with references to the required reading found in the Module 1-4 Backgrounds and Ongoing Useful Resources. In using those references, demonstrate your understanding of the concepts presented. Rather than grading on how much information you find, emphasis will be on the defense of the positions you take on the issues. Also remember that the “why” is more important than the “what” and the defense of your positions on the issues is more important than the positions you take.

Do not repeat or quote definitions. Your use of the required reading to support your opinions (that is, contentions or positions) should demonstrate that you understand the concepts presented. Do not include summaries of the readings or simply describe what the company did. Instead, your responses to the questions should be analytical and should demonstrate that (a) you understand the principles from the background reading and (b) you can apply them to this particular case. Vague, general answers will not earn a good grade.

Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make every sentence count. Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words. When writing an academically oriented paper, you will uncover many facts about the product. If you paraphrase the facts, cite the sources in your text and link those citations to references at the end of the paper.

Here are some guidelines on how to conduct information search and build critical thinking skills.

Emerald Group Publishing. (n.d.). Searching for Information. Retrieved from

http://www.emeraldinsight.com/learning/study_skill…

Emerald Group Publishing. (n.d.). Developing Critical Thinking. Retrieved from http://www.emeraldinsight.com/learning/study_skill…

Guidelines for handling quoted and paraphrased material are found at:

Purdue Online Writing Lab. (n.d.). Academic writing. Retrieved from

https://owl.english.purdue.edu/owl/section/1/2/

Purdue Online Writing Lab. (n.d.). Quoting, paraphrasing, and summarizing. Retrieved from

https://owl.english.purdue.edu/owl/resource/563/1/

Purdue Online Writing Lab. (n.d.). Is it plagiarism yet? Retrieved from

https://owl.english.purdue.edu/owl/resource/589/02…

Your paper consists of arguments in favor of your opinions or positions on the issues addressed by the guidelines; therefore, avoid the following logical fallacies:

Purdue Online Writing Lab. (n.d.). Logic in argumentative writing. Retrieved from

https://owl.english.purdue.edu/owl/resource/659/01…

make sure to reference your sources of information with both a bibliography and in-text citations. See the Student Guide to Writing a High-Quality Academic Paper,(See Attached) including pages 11-14 on in-text citations.
APA FORMAT
NO Plagiarism (will check on Turnitin)

Reference credible sources only

Module Overview

In the Module 3 SLP, you will start to develop marketing plans for the charge you chose for the session long project. In this module, we discuss distribution systems, viewed as consisting of two related issues: channels and logistics.

Distribution Channels

Many of the attributes that buyers value are provided in the channel of marketing: convenience, service, selection, and information. Accordingly, providing those values effectively and efficiently is an important concern for the marketers of branded products. Retail outlets bring their own set of attributes to the marketing mix for a product. That set also includes image and reputation.

Distribution is an important part of marketing. There is a reason you buy Wrigley’s chewing gum at the supermarket checkout line and not at a counter at Neiman Marcus. More realistically, there is a reason some companies will not allow products to be sold in certain stores. You think about a product differently when you see it at Saks Fifth Avenue compared with seeing it at Walmart.

Now the importance of consistency comes into play. Is it surprising that you cannot buy a Rolex watch at Target? Or why is your local jewelry store unlikely to sell Timex watches? Is an online retailer or limited-service discounter the place you want to buy a product that is likely to need installation or regular servicing? Why are you willing to pay $5 for a hot dog at a sporting event but shop for price at a grocery? The point is that successful marketers have everything lined up, so to speak, with the customers’ “attribute shopping list” at the time of purchase.

Services also can be distributed. For example, a veterinarian can offer care:

· At a private clinic.

· At a storefront in a mall.

· Via a mobile service, where the vet goes to your home.

· In a big animal hospital, such as Angell Animal Medical Center in Boston.

· At a university vet clinic.

· At a non-profit clinic with deeply discounted rates.

· That can be adjusted to income levels.

Each of the above choices is tailored to a specific segment. If you have a pet you care about, why do you think each of the above venues makes the people who patronize them more comfortable?

Logistics

Marketing is properly viewed as bridging gaps between producers and users, and bridging gaps in time, space, quantities, and assortments. Globalization, for example, allows people living in the northern hemisphere to enjoy fruits, vegetables, and flowers grown in the southern hemisphere, giving consumers year-round access to these products. Obviously, transportation is the primary issue in that regard; that is, bridging the space between growing roses in Colombia and enjoying them on Valentine’s Day in Chicago.

But as important as transportation is, other aspects of logistics are also important. We can eat apples (harvested months before) in the spring, thanks to the technology of cold storage. Then there is the matter of assembly, breaking bulk, and assortments. Products produced in small quantities are assembled to facilitate efficient transportation and large-scale purchases. Case lots are broken down so that consumers can buy just one or two. And foods and non-food items from literally thousands of vendors are brought together in supermarkets and department stores so that we can do our shopping in one place—and not have to spend, for example, all day Saturday visiting butchers, bakers, and greengrocers in order to get the food needed for the following week.

All of these producer/consumer gaps place a significant burden on the distribution channel in terms of inventorying quantities and assortments responsive to supplies available and expected demands—when, where, and how much—with varying cost and customer service implications. Should a retailer take advantage of discounts and purchase several months’ worth of inventory or save cash and buy on a 14-day cycle—with the possibility of a stock-out and resulting lost sales? Supply chain science is not simple, but how these problems get solved affects both profitability and customer

Module 3 – Case

DISTRIBUTION & MARKETING PLAN

Assignment Overview

Case Reading ( see attached PDF)

Avery, J., Steenburgh, T.J., Deighton, J., & Caravella, M. (2013). Adding bricks to clicks: On the role of physical stores in a world of online shopping. GfK Marketing Intelligence Review, 5(2), 29.

Birchall, J. (2009, May 14). Macy’s online sales rise 16% despite store woes. The Financial Times, pp. 16.

Clicks and bricks; retailers and the internet. (2012). The Economist, 402 (8773), 18.

Griffiths, G H & Howard, A (2008). Balancing clicks and bricks – strategies for multichannel retailers. Journal of Global Business Issues, 2(1), 69.

Katia Campo, & Els Breugelmans. (2015). Buying groceries in brick and click stores: Category allocation decisions and the moderating effect of online buying experience. Journal of Interactive Marketing, 31, 63.

Macy – online order fulfillment center expansion – Arizona. (2014). World Market Intelligence News

Macy’s fights online shopping with a tablet in fitting rooms. (2015). Engadget [Engadget – BLOG]

https://www.engadget.com/2015/08/18/macys-tablets-fitting-rooms/

Mixing bricks with clicks; retail. (2013). The Economist,406(8828), 70.

Newman, D. (2016). The Top 10 Trends Driving Marketing In 2017. Forbes. Retrieved from

http://www.forbes.com/sites/danielnewman/2016/10/18/the-top-10-trends-driving-marketing-in-2017/#34f633e17581

Case Assignment

· Briefly review the facts on the brick and click retailing and multichannel shopping, as reported in the articles.

· Describe how Macy’s performed in online channel? How Macy’s could do a better job to manage the online and offline channels? Consider appropriate distribution concepts to support your arguments.

· Considering the changing consumer shopping behaviors and new technology trends, how would you recommend Macy’s compete with Amazon and other retailers in the future?

Assignment Expectations Regarding Your References and Defense of Your Positions

Here are some guidelines on how to conduct information search and build critical thinking skills.

Emerald Group Publishing. (n.d.). Searching for Information. Retrieved from

http://www.emeraldinsight.com/learning/study_skills/skills/searching.htm

Emerald Group Publishing. (n.d.). Developing Critical Thinking. Retrieved from

http://www.emeraldinsight.com/learning/study_skills/skills/critical_thinking.htm

Guidelines for handling quoted and paraphrased material are found at:

Purdue Online Writing Lab. (n.d.). Academic writing. Retrieved from

https://owl.english.purdue.edu/owl/section/1/2/

Purdue Online Writing Lab. (n.d.). Quoting, paraphrasing, and summarizing. Retrieved from

https://owl.english.purdue.edu/owl/resource/563/1/

Purdue Online Writing Lab. (n.d.). Is it plagiarism yet? Retrieved from

https://owl.english.purdue.edu/owl/resource/589/02/

Your paper consists of arguments in favor of your opinions or positions on the issues addressed by the guidelines; therefore, avoid the following logical fallacies:

Purdue Online Writing Lab. (n.d.). Logic in argumentative writing. Retrieved from

https://owl.english.purdue.edu/owl/resource/659/01/

· make sure to reference your sources of information with both a bibliography and in-text citations. See the
Student Guide to Writing a High-Quality Academic Paper
,(See Attached) including pages 11-14 on in-text citations.

· APA FORMAT

· NO Plagiarism (will check on Turnitin)

Reference credible sources only

The following resources are not

acceptable for this course, keep in mind, there are many others:

· Wikipedia.com

· Ehow.com

· About.com

Smallbusiness.chron.com

· Diffen.com

Yourbusiness.azcentral.com

· Investopedia.com

· Boundless.com and Lumen

· Course hero

· Studypool

· Chegg

Top of Form

Rubric Name: MBA/MSHRM/MSL Case Grading Rubric -Timeliness v1

Criteria

Level 4 – Excellent

Level 3 – Proficient

Level 2 – Developing

Level 1 – Emerging

Assignment-Driven Criteria

23 points
Demonstrates mastery covering all key elements of the assignment in a substantive way.

20 points
Demonstrates considerable proficiency covering all key elements of the assignment in a substantive way.

18 points
Demonstrates partial proficiency covering all key elements of the assignment in a substantive way.

14 points
Demonstrates limited or poor proficiency covering all key elements of the assignment in a substantive way.

Critical Thinking

9 points
Demonstrates mastery conceptualizing the problem. Multiple information sources, expert opinion, and assumptions are analyzed, synthesized, and critically evaluated. Logically consistent conclusions are presented with appropriate rationale.

8 points
Demonstrates considerable proficiency conceptualizing the problem. Information sources and viewpoints of experts are proficiently analyzed and evaluated. Assumptions are clearly stated and supported, but may not be questioned. Conclusions are logical, but may be somewhat disconnected from the analysis.

7 points
Demonstrates partial proficiency conceptualizing the problem. Information sources and viewpoints of experts are stated, but not necessarily synthesized, or critically evaluated. Assumptions are stated but not supported. Conclusions may be logical, but are not connected to or supported by the preceding analysis.

6 points
Demonstrates limited or poor proficiency conceptualizing the problem. Information sources and viewpoints of experts are either absent or poorly analyzed, synthesized, and evaluated. Assumptions are implied, but not clearly stated. Conclusions are either absent or poorly conceived and unsupported.

Business Writing

4 points
Demonstrates mastery in written communication and a skilled, knowledgeable, and error-free presentation to an appropriately specialized audience.

3 points
Demonstrates considerable proficiency in written communication with a well-organized presentation to an appropriately specialized audience.

2 points
Demonstrate partial proficiency in written communication with few grammatical or syntax errors, but may lack headings or be pitched at the wrong audience.

1 point
Demonstrates limited or poor ability to write clearly, and uses poor grammar and syntax. Text may be disorganized and rambling.

Effective Use of Information

6 points
Demonstrates mastery in locating relevant and quality sources of information, using strong and compelling content to support ideas, convey understanding of the topic, and shape the whole work.

5 points
Demonstrates considerable proficiency in retrieving information, and in using appropriate and relevant content to support ideas, and convey understanding of the topic. Few arguments left unsupported.

4 points
Demonstrates partial proficiency to retrieve information, but may not be able to discriminate quality. Uses relevant content to partially support ideas, but leaves many arguments unsupported. May use immaterial or disparate content in an attempt to support arguments.

3 points
Demonstrates inability to retrieve information, or use appropriate or relevant content to support ideas, convey understanding of the topic and shape the whole work. Makes unsupported arguments and assertions.

Citing Sources

3 points
Demonstrates mastery using in-text citations of sources, proper format for quotations, and correctly format full source information in the reference list using APA style (bibliography).

2 points
Demonstrates considerable proficiency using of in-text citations of sources, proper format for quotations, and provides sufficient source information in the reference list, though not in APA format (bibliography).

1 point
Demonstrates occasional use of in-text citations of sources and provides partial reference information, such as a URL or web link
(bibliography).

0 points
Demonstrates inability to cite sources or provide a reference list (bibliography).

Timeliness

5 points
Assignment submitted on time or collaborated with professor for an approved extension on due date.

3 points
Assignment submitted 1-2 days after module due date.

2 points
Assignment submitted 3-4 days after module due date.

0 points
Assignment submitted 5 or more days after module due date.

Overall Score

Level 4
45 or more

Level 3
40 or more

Level 2
35 or more

Level 1
0 or more

Bottom of Form

Student Guide to Writing

a High-Quality Academic Paper

Follow these guidelines when writing academic papers,

including your Case and SLP assignments.

 An effective academic writing style is an essential part of a

university education.

 Poorly written papers detract from your ability to effectively share

your knowledge and ideas with others, including your professors.

 This guide will help you prepare high-quality papers that are:

▪ Logically argued

▪ Clearly structured and formatted

▪ Written in a professional, academic style

The basic structure of an academic paper includes:

1. Cover page 2. Introduction 3. Body of the

paper (which may have subsections) 4.

Conclusion 5. Reference page

The cover page of an academic paper should

include the:

▪ University name ▪ Student’s name ▪

Assignment title ▪ Course number and name

▪ Professor’s name ▪

Date

Note: Some professors recommend adding the assignment instructions

(tasks and/or questions) to the bottom of the cover page to help students

make sure they have addressed each part of the assignment.

University Name

Student’s Name

Module 1 Case Assignment

Course Number: Course Name

Professor’s Name

Date

In the introduction, provide a brief, clear overview of:

1. Each problem or issue that you will discuss

2. The solution to the problem(s) or your response to the

issue(s)

3. How you will prove or demonstrate that your solution or

response is correct

Tip: Try writing the body of your paper first. Then come back

and write the introduction once you know what your paper is

about.

 The body of the paper is where you discuss the solution to the problem(s)

or your response to the issue(s) raised in the assignment.

 After you have read the materials related to the assignment, begin by

creating a quick outline:

▪ What are the main points of your argument? Jot them down.

▪ Depending on the length of the paper, 3–6 main points should be

plenty.

▪ If a point is complex, it may have 2 or 3 sub-points. Jot those down as

well.

▪ Now arrange those points in a logical sequence.

▪ Which point needs to be made first because it provides a basis

for the points that follow?

▪ For example, “Point A leads to point B, which leads to point C, and

when A, B, and C are considered together they mean that the

solution is point D.”

Example of the structure of a Case Assignment that requires 4 pages of

text
(not including the cover page, and not including a reference page for assignments that require one):

Main Sections Points Sub-points Page # # of Paragraphs

Cover Page

Introduction 1 1

Body of Paper Point A 1 1

” Point B 2 1

” Sub-point 1 2 1

” Sub-point 2 3 1

” Point C 3 1

” Point D 4 2

Conclusion 4 1

Reference Page

In the body of your paper:

 Use headings and subheadings to help your reader follow the points and sub-

points in your discussion and to better organize sections and subsections.

 Give each point and sub-point a short name that tells your reader what that section

is about. Use those names for your headings.

 Here is a quick “how-to” guide to headings with links to examples and instructions:

http://blog.apastyle.org/apastyle/2011/04/how-to-use-fivelevels-of-heading-in-an-

apa-style-paper.html

 Now you are ready to begin writing the body of your paper.

http://blog.apastyle.org/apastyle/2011/04/how-to-use-five-levels-of-heading-in-an-apa-style-paper.html

▪ Discuss one point at a time and explain each point clearly.

▪ Discuss one point or sub-point in each paragraph.

▪ As you advance to writing more complex papers (e.g., upper-division

undergraduate or master’s-level assignments), it may take 2 or 3 paragraphs to

fully develop and support a point.

In the body of your paper:

 Each paragraph should be made up of approximately 3–5 sentences. (Note: A

single sentence is not a paragraph. Break long sentences into 2 or 3 shorter

ones.) Each paragraph should include:

▪ The point or focus of that paragraph in the first sentence

▪ Additional sentences in which you explain, elaborate, and support your point

(see section on Supporting Your Points that begins on the next slide)

▪ A conclusion/transition to the next point and paragraph

 Each point should be supported by citing and referencing the sources that provide

the foundation for your solutions and/or responses. How to do this will be

discussed on the next slide.

Supporting Your Points

 What makes an academic paper “academic”? How does an academic

paper differ from other types of writing—for example, a short story, a blog, a

newspaper article, a business letter, or an e-mail message?

 In an academic paper:

▪ You must provide support for each idea, statement, or point that you make that

is based on someone else’s ideas.

▪ Support is provided through citations and references. (References are

discussed beginning on Slide 17.) Citations appear within the paper itself

wherever you draw upon another person’s ideas or another source of

information. References are listed on a separate page at the end of your

paper.

▪ Each citation refers to a specific reference so that your reader can look up the

sources of your support and read them for himself or herself.

▪ Citations are short and usually only include the author’s last name and the

date of publication of the author’s work, for example, “In a study of K–12

education, Jones (2013) found that…”

Citation Examples

 You can cite at the beginning or ending of a sentence:

▪ According to Jones (2007), a reason for poor student performance is large

classroom size.

▪ Student performance decreases as classroom size increases (Jones, 2007).

 When multiple sources support your point, cite them together in alphabetical order

at the end of the sentence:

▪ Educators agree that large classroom size decreases student performance

(Adams, 2005; Jones, 2007; Smith, 2008).

 When a source is written by more than one person, give their last names in the

citation at the end of the sentence, like this: (Smith, Adams, & Jones, 2006).

 When there is no author and/or no date (e.g., a Web page), see this example:

http://www.apastyle.org/learn/faqs/web-page-no-author.aspx

Do not spell out the titles and publication details of your sources in the body of your

paper. Instead, provide a short citation, and add a full reference with the publication

details in your reference list. Interested readers can then find the details about the article

in your reference list at the end of your paper.

Wrong:

 The first article that will be discussed is called “The Very Separate Worlds of Academic

and Practitioner Periodicals in Human Resource Management” written by Sara Rynes,

Tamara

Giluk, and Kenneth Brown, which was published in the Academy of Management Journal

(2007) Vol 50, No.5, 987-1008. They studied the gap between academic and practitioner

knowledge.

▪ Note: Do not spell out the title and publication details of your sources in the text. Right

(two different ways):

1. Rynes, Giluk, and Brown (2007) found a gap between academic and practitioner

knowledge.

▪ Note: The authors are the subject of the sentence. This is referred to as an “in-text citation” and

includes just the authors’ last names and year of publication.

2. A gap was found between academic and practitioner knowledge (Rynes, Giluk, & Brown,

2007).
▪ Note: The citation is placed at the end of a sentence in parentheses. This is called a

“parenthetical citation.” In this type of citation, use an ampersand (&) instead of “and.”

When should you cite a source?

 When you use your own words in referring to the ideas or concepts of others

 When you use the exact words that are written in one of the sources that you read

▪ Using someone else’s exact words is called a “quotation.”

▪ For quotes of less than 40 words, use quotation marks and follow the quote with a

parenthetical citation that includes:

▪ The name(s) of the author(s)

▪ The year of publication

▪ The page number the quote was taken from in the original source— for

example:

“Academic and practitioner periodicals in human resource management are

worlds apart” (Rynes, Giluk, & Brown, 2010, p. 992).

▪ Any phrase or quote of 40 or more words should be separated from the text of

your report by single spacing and by indenting from the both right and left margin.

This is called an “offset quote.”

Provide Support for Each of Your Points

 Scholarly academic work builds on previous knowledge and recognizes the contributions that others

have made to knowledge.

 Providing a citation for each source of information that you use is necessary for at least four

reasons:

▪ To help your reader understand the foundational information that you used to support your

points.

▪ To give credit to sources of knowledge and the work of others.

▪ To protect the source. If you make a good point but don’t cite your sources or indicate direct

quotes with quotation marks, the reader will attribute it to you by default.

▪ To avoid plagiarism. Incorporating material from outside sources (whether direct quotes or

paraphrasing) without proper identification or citation is a form of plagiarism. Never represent

the work of another as your own.

 Here is an excellent guide to help you understand plagiarism and how to avoid it (students are

strongly encouraged to study it carefully):

University Libraries, University of Missouri (n.d.). Plagiarism Tutorial. Retrieved March 1, 2013,

at http://lib.usm.edu/legacy/plag/plagiarismtutorial.php

 In your conclusion:

▪ Summarize your argument regarding the solutions/responses that

you discussed in the body of your paper, including the most

important points you made and how they relate to your overall

conclusion.

http://lib.usm.edu/legacy/plag/plagiarismtutorial.php

▪ Do not discuss or raise new issues in the conclusion.

▪ Limit the conclusion to 1 or 2

paragraphs.

 The reference section, found at the end of the paper, is an alphabetical list of the

sources that you used to write your paper.

 Center the word “References” at the top of a new page.

 Starting on the same page, enter a full reference for each citation in your paper. Provide

only one reference for each source no matter how many times you cite it in your paper.

▪ Each reference should include the following information (so readers can find the

source):

▪ Author’s last name, first initial, middle initial

▪ Year of publication

▪ Title of the article, book, or Web page

▪ Title of the publication where the article was found (If the article is from a

journal or newspaper, include the volume and issue number, and the pages

where the article is located.)

Reference section formats for different types of sources:

 Article on a Web page with no date:

▪ Author last name, first initial, middle initial (publication date). Title of the article. Retrieved

date from http://

▪ Example (note that the second line of the reference is indented five spaces):

Dvoretsky, D. P. (n.d.). History: Pavlov Institute of Physiology of the Russian Academy of

Sciences. Retrieved March 1, 2013, from http://www.infran.ru/history_eng.htm

 Online newspaper article:

▪ Author name (year, month, day of publication). Article title. Newspaper Title. Retrieved X

date from http://

▪ Example (note that the second line of the reference is indented five spaces):

Hilts, P. J. (1999, February 16). In forecasting their emotions, most people flunk out. The New

York Times. Retrieved March 1, 2013, from http://www.nytimes.com

 Academic Journal Article:

▪ Author name, first initial, middle initial (publication year). Article title. Journal Title, vol.

http://www.infran.ru/history_eng.htm

http://www.nytimes.com/

#(issue #), page numbers where the article was found.

▪ Example (note that the second and third lines of the reference are indented five spaces):

Shapiro, D., Kirkman, B., & Courtney, H. (2007). Perceived causes and solutions of the

translation problem in management research. Academy of Management Journal, 50(2), 249-

266.

 Book: Author name (publication year). Book Title. Location: Publisher.

▪ Example: Fitzgerald, S. P. (2002). Decision Making. London: Capstone Publishing, Ltd.

Reference Page Example

References

Allen, G. (1998). Motivating Supervision. Retrieved March 1, 2013, from:

http://www.businessballs.com/mcgregoryxytheorydiagrm

Chapman, A. (n.d.). Adam’s Equity Theory. Retrieved March 1, 2013, from:

http://www.businessballs.com/adamsequitytheory.htm

Chapman, A. (n.d.). Herzberg’s Motivation Theory. Retrieved June 1, 2009, from:

http://www.businessballs.com/herzberg.htm

Dreyfack, R. (2004, May). Personalizing productivity. Supervision, 65(5), 20-22.

http://www.businessballs.com/mcgregoryxytheorydiagrm

http://www.businessballs.com/adamsequitytheory.htm

http://www.businessballs.com/herzberg.htm

Shapiro, D., Kirkman, B., & Courtney, H. (2007). Perceived causes and solutions of the

translation problem in management research. Academy of Management Journal,

50(2), 249-266.

Notes:

▪ “n.d.” = no date. Use this for the date when there is no publication date available.

▪ First line of each reference is at the left margin, and each subsequent line in that

same reference is indented 5 spaces (one tab stop).

▪ Arrange references alphabetically based on last name of the first author of each work.

 Add an appendix after the reference page when you have supplemental

material (e.g., a chart, table, diagram, or picture) that you refer to in your

paper.

 Appendices are optional and depend upon the nature of the assignment.

 Appendices (if any) should be placed at the end of the paper and identified

with capital letters (e.g., Appendix A).

 The title of the appendix should be placed immediately below the appendix

label.

 The appendix label and title should be centered at the top of the page, as in

the example below:

Appendix A

Workflow Diagram

 When professors ask you to “follow APA style” or “use APA format,” they are

referring to the Publication Manual of the American Psychological Association, Sixth

Edition.

 APA is one of several styles that is used for writing academic papers (MLA is

another) and includes extensive details about how to format citations and references.

 APA format is required for doctoral students and recommended for University

master’s and undergraduate students.

 APA helps to provide a common, standard format for academic scholars to follow.

 For additional information and guidance on APA style, here are two excellent

resources:

▪ The APA Style website at http://www.apastyle.org (see the links and tutorials at the

bottom of the Web page)

▪ The Purdue Online Writing Lab (http://owl.english.purdue.edu/owl/resource/560/01/)

contains extensive, detailed guidance not only on APA format, but also on general

http://www.apastyle.org/

http://owl.english.purdue.edu/owl/resource/560/01/

writing, job search writing, and research writing (see the tabs at the top of the Web

page).

Set up your paper as follows:

 Set 1-inch margins on all four sides.

 Use 12-point type throughout; don’t use different type sizes.

 Double-space the text throughout the paper, including the reference page.

 Do not put extra spaces between paragraphs or between headings and

paragraphs.

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 Re-read the assignment instructions and make sure you addressed

each one in your paper.

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your assignment.

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structure, invite a classmate or colleague with strong English writing skills

to proofread your work prior to submission. This process will improve your

writing skills.

 Also, consult the Purdue Online Writing Lab (OWL) for writing guidance and

examples.

 Don’t expect overnight miracles. Writing and editing are iterative processes

that take ongoing practice, feedback, refinement, and attention to detail—

even for the best writers. Your writing will improve as you advance through

the program!

Macy’sonline sales rise 16% despite store woes

Birchall, Jonathan. Financial Times; London (UK) [London (UK)]14 May 2009: page 16.

general retailers

Macy’s, the largest US department store chain, yesterday underlined the increasing lure of online

sales to embattled US retailers, as it reported a 16 per cent increase in quarterly sales on its

website, even as store sales fell 9.5 per cent.

The retailer stepped up investment in its online business over the past two years, after ending an

initial online partnership with Amazon. Terry Lundgren, chief executive, said that in the future more

of Macy’s capital expenditure would be directed online as expansion of its 840-strong network of

physical stores slows.

“In the future as we project out, more of the capital resources will go in that direction . . . I do think

over time new stores, and new mall construction sites, are going to take longer to become a reality,”

he said.

Macy’s online sales grew by 29 per cent last year, even as its overall sales fell 7 per cent. It did not

give a total for its 2008 online sales, but industry estimates put them at about $1bn.

David Fry, founder of Fry, a web services company, notes the recession has seen increased

variation in the online performance of retailers, who could previously count on double-digit growth

from their online business.

JC Penney, for instance, reported flat year-on-year internet sales of $1.5bn in 2008 as its

comparable store sales fell 8.5 per cent. But Gap, the clothing retailer, saw a 14 per cent increase in

direct sales, to $1bn, while its comparable sales fell 12 per cent.

This month, Amazon, the largest US online retailer, said its North American sales had increased by

21 per cent during its first quarter. Wal-Mart has also reported strong sales online.

In addition to providing growth opportunities for retailers with a large physical store presence, such

as Macy’s, industry consultants say they are seeing a surge of interest in retailers expanding online

operations.

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Janet Hoffman, a retail consultant with Accenture, said she had seen increased interest from

retailers in combining online presence with physical stores through initiatives such as store delivery

for online purchases.

“When you look at the capital expenditure required for new stores . . . clearly it makes sense to

extract value from your existing investments,” she said.

Eddie Lampert, CEO of Sears Holdings, highlighted his company’s online strategy at Sears and

Kmart department stores at the company’s annual meeting last month. Sears is currently testing a

new online service, called Mygofer, and has opened a pilot store in Illinois focused on providing

physical delivery of goods ordered on the internet.

In an indication of the widening appeal of online business, Dollar Tree, one of America’s hard-

discount dollar stores, recently launched online bulk sales of its household and office goods, with

free store delivery.

A recent survey by Shop.org and Forrester found that almost half of the 117 retailers surveyed had

no plans to cut online marketing, despite the recession.

Credit: By Jonathan Birchall in New York

Mixingbricks with clicks; Retail

Anonymous. The Economist; London Vol. 406, Iss. 8828, (Mar 23, 2013): 70.

Abstract
Translate [unavailable for this document]

Multichannel (or even better, omnichannel) is something almost every self-respecting retailer wants

to be. It means letting customers shop with smartphones, tablets, laptops and even in stores as if

waited upon by a single salesman with an unfailing memory and uncanny intuition about their

preferences. Pure-play internet vendors are good at this. But most resist the idea that actual stores,

with their rents, payrolls and security cameras, ought to be one of those channels.

Full Text
Translate [unavailable for this document]

Some online retailers are venturing onto the high street

KIDDICARE wants to be as disruptive as the little monsters who use its products. Traditional sellers

of baby gear, laden with too many stores and creaky technology, have all the perkiness of sleep-

deprived parents. Internet-based Kiddicare should run rings around them. So it seemed odd last

year when the British merchant took over ten “superstores” from Best Buy, itself an erstwhile

disrupter (in electronics). Far from weighing Kiddicare down like overstuffed nappy bags, the shops

will give customers “a true multichannel experience”, the retailer vowed.

“Multichannel” (or even better, “omnichannel”) is something almost every self-respecting retailer

wants to be. It means letting customers shop with smartphones, tablets, laptops and even in stores

as if waited upon by a single salesman with an unfailing memory and uncanny intuition about their

preferences. Pure-play internet vendors are good at this. But most resist the idea that actual stores,

with their rents, payrolls and security cameras, ought to be one of those channels. The thought of

having the same costs as bricks-and-mortar competitors “scares the living daylights out of me,” says

Charles Hunt, owner of Duvet and Pillow Warehouse, a fast-growing online retailer.

Yet Kiddicare, owned by Morrisons, a British grocer, is not the only retailer to shed its online purity.

Screwfix, a British supplier to plumbers and electricians, has opened 270 shops since 2005.

Bonobos, which sells men’s clothing online, has opened several “Guideshops” in America. Zalando,

a German online fashion store, opened a physical outlet in Berlin last year. Even Amazon has

installed lockers in shopping malls where customers can pick up deliveries: a first step, perhaps,

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http://www.economist.com/

towardsbricks-and-mortardom. All this suggests that online and traditional retailers are “migrating to

a middle ground”, believes Matt Truman of True Capital, a fund that invests in consumer companies.

Don’t try this on at home

For wares that do not have to be displayed in a showroom, online retailers are hard to beat. They

killed Borders, an American bookstore chain, and Britain’s Comet, an electronics retailer. But it is

easier to judge a shoe’s fit or an apple’s crispness in a real store. Shoppers who crave instant

gratification will not get that online. Tradesmen are last-minute shoppers, which is why Screwfix, part

of the Kingfisher DIY group, has so many shops. Car seats must be fitted and parents like to handle

baby equipment before they buy it; hence Kiddicare’s expansion beyond a single flagship store.

Pure online retailers do not pay rent but their variable costs eat up much of that advantage, says

Sophie Albizua of eNova Partnership, a consultancy. Without storefronts to lure in customers they

shell out to buy ads linked to Google search results. Delivery, especially of bulky goods, is a

headache. Couriers show up at empty houses, and fees often fail to cover the full cost. Shoppers

return a quarter or more of clothing they buy, another big expense.

All this looks easier if you have real shops. With “click and collect” customers can order with, say, a

smartphone but pick up the item at a convenient outlet. Often, they linger to shop more. Britons pick

up something extra about 40% of the time, says Ms Albizua.

Happily hybrid John Lewis, an upmarket department-store chain, says that on- and offline shopping

spur each other on. When a new shop opens, online sales in the vicinity can jump by 20-40%

“overnight”, says Noel Saunders, the manager of the branch near London’s Olympic Stadium. New

products can be tested online and stocked in store if they do well. Nearly a third of customers who

order online pick up their wares in stores. Britons are among the world’s most avid online shoppers,

but 65% still prefer buying in-store, according to a survey by Hitachi Consulting.

The question for envious e-tailers is how to pluck the benefits of physical stores without incurring the

costs. Most proceed gingerly, armed with high-tech weaponry. “Pop-up shops” generate buzz and

then vanish. EBay has tried them, and Winser London, a fashion website, plans to. Amazon’s

ghostly high-street presence helps make delivery cheaper and more convenient, but so far it offers

nothing more. Kiddicare plans 15 stores at most in Britain, a fraction of the number operated by its

struggling competitor, Mothercare. They will be nimbler than traditional stores. Prices will appear on

electronic labels and change with the push of a button.

Bricks-and-mortar merchants are likewise paring space and bulking up on technology. In Britain the

number of outlets a retail chain needs to have national coverage has dropped from 200 in the pre-

online era to 50-80, says Adrian D’Enrico of AXA Real Estate, an investment manager. House of

Fraser is experimenting with shops that are little more than a changing room and rows of screens to

order clothes. Hointer, a Seattle start-up, provides just enough space to display a sample of each

type of jeans it sells; robots fetch the right size from the stockroom. On today’s high street,

shopkeepers who stand still are unlikely to survive.

Module 3 – Background

DISTRIBUTION & MARKETING PLAN

The following articles explain and illustrate the role of distribution in marketing decisions.

Marketing Channels. (2014). Pearson Learning Solutions, New York, NY. Retrieved from

http://www.pearsoncustom.com/mct-comprehensive/asset.php?isbn=1269879944&id=12275

Marketing Channels (Audio). (2014). Pearson Learning Solutions, New York, NY. Retrieved from

http://www.pearsoncustom.com/mct-comprehensive/asset.php?isbn=1269879944&id=11766

Retailing and Wholesaling. (2014). Pearson Learning Solutions, New York, NY. Retrieved from

http://www.pearsoncustom.com/mct-comprehensive/asset.php?isbn=1269879944&id=11537

Retailing and Wholesaling (Audio). (2014). Pearson Learning Solutions, New York, NY. Retrieved from

http://www.pearsoncustom.com/mct-comprehensive/asset.php?isbn=1269879944&id=11767

Distribution decisions (2009). KnowThis. Retrieved from

http://www.knowthis.com/principles-of-marketing-tutorials/distribution-decisions/

Marketing mix: Place (2011). LearnMarketing. Retrieved from

http://www.learnmarketing.net/place.htm

Perner, L. (n.d.). Distribution: Channels and logistics. Introduction to marketing. Marshall School. USC. Retrieved from

http://www.consumerpsychologist.com/intro_Distribution.html

Ramachandrin, S., & Trachtenberg, J. A. (2012). End of Era for Britannica. Wall Street Journal(March 14):B1.

Ramsey, M. (2012). Glut of small cars tests Ford resolve. Wall Street Journal (January 11): B1.

Timberlake, C., & Townsend, M. (2012). Macy’s says Martha’s dance card is too full. Business Week (February 28).

Optional Reading/Resources

The following articles illustrate use of the concepts studied in this module:

Halkias, M. (2011). J.C. Penney buys stake in Martha Stewart’s company. The Dallas Morning News(December 7). Retrieved from

http://www.dallasnews.com/business/retail/20111207-j.c.-penney-buys-stake-in-martha-stewarts-company.ece

JoS. A. (2011). Bank Clothiers expands its Internet channel to ship orders to international customers. Investment Weekly News. (May 21), 698.

With Its New Music Storage and Player, Can Amazon Deliver in the Cloud? (2011, May 11).Knowledge@Wharton. Retrieved from

http://knowledge.wharton.upenn.edu/article.cfm?articleid=2768

Macy- Online Order Fulfillment Center Expansion –

Arizona
World Market Intelligence News; London [London]01 Sep 2014.

Macy’s, Inc. (Macy) has undertaken the Online Order Fulfillment Center Expansion project at cross

streets between South Sarival Avenue and South 166th Avenue, Goodyear, Arizona, the US.

The project involves expansion of existing facility over an area of 33,445m2 site. It includes the

construction of warehouses, office, parking space and related infrastructure, and the installation of

elevators.

In second quarter of 2013, the Layton Companies, Inc. has been appointed as the construction

contractor.

In May 2014, construction works have been completed.

Stakeholder Information:

Planning Authority: Goodyear City Council

Construction contract: The Layton Companies, Inc.

Project value: 35.00

Currency code: USD

Start date: 7/1/2012 12:00:00 AM

End date: 6/30/2014 12:00:00 AM

Project status: Construction Complete

Country: United States

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Balancing clicks and bricks – strategies for multichannel retailers
Griffiths, G H;Howard, A
Journal of Global Business Issues; Winter 2008; 2, 1; ProQuest Central
pg. 69

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

29Case Study / Vol. 5, No. 2, 2013, pp. 28 – 33 / GfK MIR

Buying a product has never been easier. Consumers can shop
online, over the phone or via mail order, from home or on
the go, and if they want to experience touch and feel, they
can also visit a “real” store. Often, one and the same retailer
offers several of these options, and multichannel retailing
has become common in most product categories. By offering
several channels, retailers are trying to reach more consumer
segments and create synergies, with stores acting as bill-
boards for the brand, catalogs providing enticing reminders
to buy and the Internet providing an ever-present storefront.

But synergies do not arise automatically. Different channels
can also cannibalize one another, and it is not always easy
to predict which effects will prevail. A recent study took a
closer look at the interplay among different retail channels
and showed that the short-term effects of store openings can
be very different from the long-term sales impact.

Analyzing the effects of store openings for a fashion
and home furnishings retailer /// The researchers stud-
ied the effects of store openings of one multichannel retailer
of fashion, home furnishings and high-end accessories. This
retailer sold directly to consumers through catalogs and the
Internet, as well as operated stores in shopping malls in some
regions of the U.S. During the observation period – a time –
span of nearly nine years – the retailer opened four new stores
in the U.S. Two of the stores launched operations in retail
trading areas previously served by only the direct channels,
and two were opened in areas near a pre-existing retail store.

Adding Bricks to Clicks:
On the Role of Physical Stores in a

World of Online Shopping
Jill Avery, Thomas J. Steenburgh, John Deighton and Mary Caravella

key words

Multichannel Retailing, Channel Management,
Channel Migration, Direct

Marketing, E-Commerce, Retail Stores

•

the authors

Jill Avery,
Harvard University,

javery@hbs.edu

Thomas J. Steenburgh,
University of Virginia,

SteenburghT@darden.virginia.edu

John Deighton,
Harvard Business School, Harvard University,

ejdeighton@hbs.edu

Mary Caravella,
University of Connecticut,

mary.caravella@business.uconn.edu

— doi 10.2478 / gfkmir-2014-0015
OPEN

new store A
Neighboring store

figure 1:

Comparing sales effects across channels in areas
with and without new stores

Control
region

Control
region
Control
region
Control
region

To isolate the effects of the store openings
or rule out alternative explanations for sales changes,

the control groups were actively composed
to match the group exposed to the store opening.

new store b
No neighboring store

new Store C
Neighboring store

new Store D
No neighboring store

GfK MIR / Vol. 5, No. 2, 2013, pp. 28 – 33 / Case Study

31Case Study / Vol. 5, No. 2, 2013, pp. 28 – 33 / GfK MIR

The study documents how sales in all channels reacted to the
opening of the new stores by comparing them with a control
sample that matches each group on several relevant criteria
(geographic, demographic, behavioral, sales and competitive
variables as well as marketing activity). Online and catalog
orders originating from the regions where the new stores
were located were assigned to four groups. All purchasers
with resident ZIP codes within a maximum of 60 minutes
driving time to the nearest new store were selected.

Complementary effects outbalance channel cannibaliza-
tion /// The opening of brick-and-mortar stores by this retailer
had positive and negative effects, but complementary conse-
quences clearly outbalanced sales drops in individual channels:

> In the short run, only catalog sales declined in the trading
areas of the new stores. They dropped by 11.9 % on average
shortly after the brick-and-mortar stores opened.

> Internet channel sales did not drop after the stores opened
for business.

> Over time, both channels

increasingly benefited from the

presence of the new brick-and-mortar stores. Within 79
months, catalog sales recovered to the level that would
have been expected had the store never opened and sub-
sequently continued growing more than in the sample
without new stores.

> The store openings had a greater positive impact on the
Internet channel than on the catalog channel. Its comple-
mentary effect was approximately five times larger.

> Interestingly, areas with pre-existing stores gained more
from the additional store than virgin territory. Obviously,
branding effects from the two stores added up, significantly
impacting brand awareness. The existence of additional
stores seemed to support the brand image and accelerate
growth. Research results showed that having more stores
in an area can offer value for online sales as well. Store
placement decisions should therefore reflect these effects
in addition to calculations indicating whether an area can
generate incremental sales to cover costs.

Over time, both channels

increasingly benefited from the

presence of the new

brick-and-mortar stores.

figure 2:

Overview effects of additional stores

Short-term sales long-term sales

Catalog channel 11.9 % drop within
first month

Full recovery after
79 months, continued
growth thereafter

Internet channel No drop Growth effect of
store opening five
times greater than
for catalogs

32 GfK MIR / Vol. 5, No. 2, 2013, pp. 28 – 33 / Case Study

> The number of first-time customers shopping via direct
channels was not immediately affected by the opening of
retail stores but did increase over time. The store seemed
to act as a billboard for the direct channels by attracting
new customers to the retailer at a faster rate than would
have been expected had the store never opened.

> There was more cannibalization from the direct channels in
areas without pre-existing stores. Some customers chose
to try out the brand in the store instead of the direct chan-
nels in virgin territories, taking advantage of an opportu-
nity they did not have before. In regions with pre-existing
stores, this cannibalization seemed to have had occurred
when the original store first opened. Over time, however,
the new stores attracted customers to the brand at a faster
rate in both types of territories.

Tracking the dynamics

of store openings helps to implement

the desired effects

for the company and its customers.

33Case Study / Vol. 5, No. 2, 2013, pp. 28 – 33 / GfK MIR

—

Managerial summary of an article published in the
academic top journal “Journal of Marketing”:

Avery, Jill; Steenburgh, Thomas J.; Deighton, John;
Caravella, Mary (2012): “Adding Bricks to Clicks:
Predicting the Patterns of Cross-Channel Elasticities
over Time”, Journal of Marketing, Vol. 76, No. 3 (May),
pp. 96 – 111.

Integrated channel management optimizes results ///
Even though this study involved only store openings by a
single retailer with a well-established brand, some general
implications are applicable across the board: A stronger
understanding of positive and negative cross-channel effects
helps retailers to better anticipate and respond to changes in
sales in existing channels when a new one is added. It is the
basis for strategically managing a company’s channels as a
portfolio rather than as separate entities.

An integrated approach to customer acquisition and
relationship management benefits all parties. Tracking the
dynamics of store openings helps to implement the desired
effects for the company and its customers. In the case under
review, the opening of a retail store had a small impact on the
rate at which first-time customers used the direct channels in
the short run. Therefore, managers of direct channels should
continue to invest in customer acquisition programs during the
months surrounding a new store opening if the retailer finds it
more profitable to serve customers in the direct channels than
in stationary stores. In the long term, retail stores increase the
rate at which first-time direct channel customers are acquired.
Thus, prospecting materials for new direct channel customers
should include a retailer’s brick-and-mortar store location and
should highlight cross-channel benefits, such as the ability to
pick up or return items ordered online at the store.

Adding brick-and-mortar stores creates more conflict with the
catalog than with the online channel. In the short run, manag-
ers of catalog channels might be entitled to some temporary
relief in their revenue targets because of the store openings to
ensure their cooperation. In the long run, however, managers
of catalog channels should be supportive of store openings.
Catalog sales recover and ultimately benefit from the brand-
ing effects of the brick-and-mortar store.

Cross-channel promotions should be considered to lever-
age synergies. Given that stores enhance sales in the direct
channels over time, promotions that encourage customers
to shop across channels should be implemented. Cooperative
cross-channel marketing can improve sales in all channels
or drive sales from less profitable channels to more profit-
able ones. For example, if catalog cannibalization is undesir-
able to the retailer, it can offset drops in sales by increasing
direct-channel promotions in the surrounding area during
the store’s opening period. This may keep existing customers
in the catalog channel rather than entice them to shop in the

store. In this case, the store becomes a customer acquisi-
tion engine, with its promotional vehicles targeted toward
new, rather than existing, customers. Once customers are
won over, communication can eventually direct them to the
lower-cost catalog and online channels.

Models and algorithms used to drive catalog and other direct
mailings to customers should reflect cross-channel effects
in their decision-support systems to avoid a counterproduc-
tive decrease in marketing support. Traditional RFM models,
which recommend catalog or direct-mail drops based on the
recency, frequency and monetary (RFM) value of prior trans-
actions, might lead to the implementation of counterpro-
ductive marketing activities. Retailers may decrease catalog
mailings to customers who have temporarily switched some
of their purchasing to the retail store channel. Particularly
in the catalog channel, this decrease in marketing support
may intensify the drop in sales and prolong the onset of
synergistic effects. Retailers who understand the patterns
of cross-channel interaction can adjust the algorithm and
reinforce synergies.

Clicksand bricks; Retailers and the internet

The Economist; London Vol. 402, Iss. 8773, (Feb 25, 2012): PG 18.

Abstract
After a panic at the turn of the millennium about the impact on the retailing industry of online

shopping, bricks-and-mortar stores settled into making only modest alterations to their business

model or, ostrich-like, trying to ignore it. Few have so far made the radical changes needed to meet

the threats from, and tap the enormous potential of, e-commerce. Such inaction threatens retailers’

survival. Online sales are now approaching $200 billion a year in America. To build a profitable

online business retailers must integrate it seamlessly with their bricks-and-mortar operations. Many

keep them separate, increasing the risk that they fail to communicate or work together properly.

Full Text
Translate [unavailable for this document]

Many retailers are being too slow in reinventing themselves for the age of online shopping

“WE TEND to overestimate the effect of a technology in the short run and underestimate the effect in

the long run,” observed Roy Amara, an American futurologist. This is certainly proving true of

retailers and their attitude to the internet. After a panic at the turn of the millennium about the impact

on their industry of online shopping, bricks-and-mortar stores settled into making only modest

alterations to their business model or, ostrich-like, trying to ignore it. Few have so far made the

radical changes needed to meet the threats from, and tap the enormous potential of, e-commerce.

Such inaction threatens retailers’ survival. Online sales are now approaching $200 billion a year in

America. Their share of total retail sales is creeping up relentlessly, from 5% five years ago to 9%

now. People in their 20s and 30s do about a quarter of their shopping online. True, few ladies who

lunch will buy their Christian Dior dresses online; and bargain-hunters will still enjoy rummaging in

discount stores like Dollar General. But to attract everyone in between, retailers will have to build a

strong online offering while making their shops nicer, more conveniently located and, in the case of

many big-box retailers, smaller. Otherwise they are likely to go under, as United Retail Group, an

American clothing chain, did this month.

To build a profitable online business retailers must integrate it seamlessly with their bricks-and-

mortar operations. Many keep them separate, increasing the risk that they fail to communicate or

work together properly. Walmart’s online operations are in Silicon Valley, far from its Arkansas

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headquarters. Target, another supermarket giant, until recently outsourced its e-commerce to

Amazon, the biggest online retailer, and is only now building its own e-business. Both Walmart and

Target still have a puny online presence relative to their size.

Are you being served?

Retailers also need to be ruthless in chucking out products that do not gain from being sold in a

physical store: not just things like CDs and DVDs, which can be replaced by digital goods, but bulky

stuff like nappies (Amazon has become a big seller of Pampers). Their shops must focus on those

things, such as expensive clothes and gadgets, that customers will want to try before they buy, and

for which they will pay extra, such as advice from competent sales assistants.

Stores have to become more fun to visit, so shoppers feel it is worth the trip to the mall or high

street. Apple’s shops thrive not only because they contain cool products; they are beautifully

designed, with helpful staff. Disney stores may be an ordeal for parents but they often succeed in

giving their pint-sized clients “the best 30 minutes of a child’s day”. But too many retailers think only

of getting a quick sale, neglecting to build relationships with customers. They are the most at risk

from “showrooming”: shoppers trying products in physical stores before sneaking off to buy them

more cheaply online.

To survive in the new world of retail shopkeepers will need large amounts of imagination–and

money. Macy’s is investing $400m in the renovation of its flagship store in New York. The losers will

include those (like Borders, an extinct chain of bookshops) that keep selling things people are happy

to buy online. The biggest winners will be consumers. They can look forward not only to ever-greater

convenience thanks to the internet. They will also find a growing number of physical stores that

compete to make shopping a pleasure.

⁎ Corresponding author.
E-mail addresses: katia.campo@kuleuven.be (K. Campo),

els.breugelmans@kuleuven.be (E. Breugelmans).

www.elsevier

http://dx.doi.org/10.1016/j.intmar.2015.04.001
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Available online at www.sciencedirect.com

ScienceDirect
Journal of Interactive Marketing 31 (2015) 63–78

.com/locate/intmar

Buying Groceries in Brick and Click Stores: Category
Allocation Decisions and the Moderating Effect of Online

Buying Experience

Katia Campo ⁎& Els Breugelmans

KU Leuven, Faculty of Economics and Business, Korte Nieuwstraat 33, 2000 Antwerp, Belgium

Available online 27 August 2015

Abstract

The large majority of online grocery shoppers are multichannel shoppers who keep visiting offline grocery stores to combine convenience
advantages of online shopping with self-service advantages of offline stores. An important retail management question, therefore, is how these
consumers divide grocery purchases across the retailer’s online and offline channel. We provide a comprehensive analysis of the impact of
category characteristics on the allocation pattern of multichannel grocery shoppers and find that category allocation decisions are affected not
only by marketing mix differences between the online and offline channel, but also by intrinsic category characteristics like perceived purchase risk
and shopping convenience. In addition, we examine the effect of online buying experience. In line with expectations, we find that it can affect
allocation patterns in different ways: (i) it attenuates the perceived risk of buying sensory categories online, thereby reducing differences in online
category share, (ii) it reinforces marketing mix (assortment) effects, thereby making online category share differences more pronounced, and (iii) it
has no effect for factors such as promotions that are easy to evaluate without experience, thereby leaving the online category share stable. In
addition to different experience effects across allocation factors, we also observe variations in experience effects across consumer segments.
© 2015 Direct Marketing Educational Foundation, Inc., dba Marketing EDGE. All rights reserved.

Keywords: Multichannel shopping; Online grocery shopping; Category allocation decision; Buying experience

Introduction operate an online store next to their offline supermarket outlets

While lagging behind in comparison with many other
consumer markets, online shopping for groceries has increased
dramatically over the last few years, and now tops the agenda of
all major grocery retailers (Warschun 2012). “[Grocery] retailers
are increasingly finding they must innovate in ways that make it
easier and more convenient for their customers to get what they
need without missing a beat,” according to Nielsen’s Continuous
Innovation report, which found that “convenience itself may be
the most creative and energetic example of retail innovation”
(Nielsen 2014). Of these convenience-oriented retail innovations,
the shift towards multichannel offline-online retailing is one of
the most important and successful practices. Several of the large
grocery retail chains (such as Walmart, Tesco and Ahold) now

(‘brick and click’ grocery retailers). By increasing their service
levels, multichannel retailers aim to retain existing customers
and gain new customers in the increasingly competitive retail
environment (Chintagunta, Chu, and Cebollada 2012; Kabadayi,
Eyuboglu, and Thomas 2007; Neslin and Shankar 2009; Zhang
et al. 2010).

Customers clearly appreciate and take advantage of this
extended service. The large majority of online grocery shoppers
are multichannel shoppers who visit both the online and offline
channel, thereby combining convenience advantages of online
shopping with self-service advantages of offline stores (Alba
et al. 1997; Chu, Chintagunta, and Cebollada 2008; Chu et al.
2010; Konuş, Verhoef, and Neslin 2008; Venkatesan, Kumar,
and Ravishanker 2007). Although multichannel shoppers visit
both channels, their purchase behavior tends to differ across the
online and offline channel, both in the tendency to buy certain
categories and in the sensitivity to marketing mix instruments.
For instance, a product’s online intangibility can result in low(er)

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64 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

online purchase shares, especially for sensory categories that
consumers prefer to physically examine before purchasing them
(Degeratu, Rangaswamy, and Jianan 2000). Bulky and heavy
categories, in contrast, tend to be top-selling categories in online
stores because of the high online shopping convenience benefits
(Chintagunta, Chu, and Cebollada 2012). Prior research has also
shown that households tend to be more brand loyal and size
loyal, but less price sensitive in the online channel than in the
offline channel (Chu et al. 2010). Because channel differences in
assortment and price can vary across categories, this may also
influence consumers’ allocation patterns over the online and
offline channel. As a result, the multichannel shopping context
clearly adds to the complexity of retailers’ management
decisions, and multichannel grocery retailers need more insight
into how shoppers allocate their purchases across their online and
offline stores (cf. Dholakia et al. 2010; McPartlin and Dugal
2012; Shankar and Yadav 2010).

The purpose of this study is to improve the understanding of
multichannel shopping behavior and to provide a better insight
into the underlying mechanisms and factors that determine how
multichannel shoppers allocate their category purchases across
the online and offline channel. Building on the multiple store
and online shopping literature, we analyze the impact on
purchase allocation patterns at the category level, and take
‘traditional’ marketing mix based factors as well as ‘intrinsic’
category characteristics into account. Given that online grocery
shopping is still in the ‘innovation stage’ (small, but rapidly
increasing number of consumers who start buying groceries
online), our model explicitly accounts for dynamic adjustments
of allocation patterns as consumers gain more experience with
buying groceries online. We also account for the possibility that
managers adjust category assortment and pricing decisions to
anticipated channel differences in buying behavior, and correct
for potential endogeneity biases in marketing mix effects.

Our research provides important contributions to the marketing
and retailing literature. First, we extend insights from the multiple
store shopping literature by examining category allocation
decisions in a substantially different multichannel retail context,
with fundamental differences in the factors driving purchase
allocation decisions. Second, we add to the multichannel literature
by providing a comprehensive analysis of the factors that can
cause differences in online purchase tendency across grocery
categories. As indicated in previous (offline) purchase behavior
studies (Hoyer and MacInnis 2010), grocery shopping differs
substantially from other purchase contexts. As the same products
are purchased repeatedly, purchase involvement tends to be low,
and consumers are not prepared to spend much time and effort to
search for the ‘best’ product. Findings of previous multichannel
studies – which mainly focused on durable goods – are therefore
not directly transferrable to, and provide little insight into, what
drives purchase allocation decisions in a multichannel grocery
shopping context. The limited number of studies on multichannel
purchases of groceries focused on specific issues such as channel
differences in sensitivity to specific marketing mix instruments
(e.g., price sensitivity: Chu, Chintagunta, and Cebollada 2008

;

Chu et al. 2010), the degree of brand exploration across both
channels (Chu et al. 2010; Pozzi 2012) or the impact of transaction

costs on channel choice (Chintagunta, Chu, and Cebollada 2012).
While useful to develop expectations on the impact of specific
factors, they do not provide insights into the overall purchase
patterns of multichannel shoppers. Third, we refine and extend
previous research on online buying experience effects (Ansari,
Mela, and Neslin 2008; Frambach, Roest, and Krishnan 2007;
Kim, Ferrin, and Raghav Rao 2008) by examining experience
effects on category level purchase decisions and by taking
different possible effects of experience into account.

From a managerial point of view, our results help multichannel
retailers to improve the mix of customer services and enhance
their overall value proposition for multichannel shoppers (Zhang
et al. 2010). Our results can guide online category management
and promotional decisions of multichannel retailers to stimulate
online purchases. Striving for larger online shopping baskets can
be beneficial and generate additional revenue that may cover the
high fixed costs that online retailers face (e.g., storing and delivery
costs). Next, by obtaining a better insight into the effects of
experience on different types of factors that influence consumers’
category purchase allocation decisions, multichannel retailers can
better assess the importance of stimulating trial and repeat
purchases (to generate positive experience effects) vs. taking
corrective actions (e.g., adjust channel differences in assortment
and/or price).

Conceptual Framework

In this section, we provide a conceptual framework on how
multichannel shoppers allocate category purchases across the
online and offline channel operated by a single retailer. We take
the overall allocation of grocery purchases across channels
(channel choice and visit frequency) as given and examine
whether and how category-specific allocation factors lead to
deviations from the overall allocation scheme (i.e., result in
disproportionately low or high channel shares in category
purchases). Building on the multiple store and multichannel
shopping literature, we explain category allocation decisions as
the outcome of a shopping utility maximization process that
accounts for (i) acquisition utility, i.e., the benefits that consumers
receive (e.g., product quality and promotions) and the costs they
need to give up (e.g., price) when acquiring the product, and
(ii) transaction utility, i.e., the benefits consumers receive
(e.g., time-saving home delivery systems) and the cost they need
to bear (e.g., perceived risk of online ordering) when transferring
the products from the store to home (Baltas, Argouslidis, and
Skarmeas 2010; Chintagunta, Chu, and Cebollada 2012; Gupta
and Kim 2010; Vroegrijk, Gijsbrechts, and Campo 2013). Below,
we identify the major acquisition and transaction utility related
factors and discuss how they are expected to influence category
allocation patterns over the online and offline channel. Next, we
discuss how online buying experience in the category plays a
moderating role (see also Fig. 1).

Acquisition Utility: The Impact of Marketing Mix Instruments

Studies on multiple store shopping behavior in an offline
context have demonstrated that marketing mix based differences

Fig. 1. Conceptual framework & expected effects.

65K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

in acquisition utility – such as assortment and price differences –
are important drivers of category allocation decisions across
stores (Gijsbrechts, Campo, and Nisol 2008; Vroegrijk,
Gijsbrechts, and Campo 2013). As explained in more detail
below, even though online and offline stores that belong to the
same chain have a similar price/quality positioning, marketing
mix instruments can still differ across channels for several
reasons (Neslin et al. 2006; Wolk and Ebling 2010). In the
following, we discuss the impact of channel differences in
assortment, price and promotion intensity (Fox and Hoch 2005)
and examine the differential effect of in-store incentives aimed
at stimulating unplanned purchases on allocation decisions
(Breugelmans and Campo 2011).

Assortment Differences
Online and offline assortments can differ in size for several

reasons. On the one hand, online stores provide the opportunity to
carry a larger assortment as a result of the online store’s limitless
shelves. On the other hand, cost and demand constraints, and the
need to respect very short delivery times, can be reasons to restrict
online assortments for some categories (such as groceries). The
literature on assortment effects suggests that larger assortments
tend to be preferred over smaller ones because they offer more
choice flexibility and enhance feelings of autonomy (Oppewal
and Koelemeijer 2005; Sloot, Fok, and Verhoef 2006)1. We
expect that channel differences in assortment size can influence
channel allocation decisions, such that consumers are more

1 While larger assortments may also come at the cost of more difficult
evaluation processes because of information overload, choice conflict or regret
(Dhar 1997; Huffman and Kahn 1998), the general expectation appears to be
that the advantages of larger assortments tend to cancel out potential
disadvantages (cf. negative effects of assortment reductions on category sales;
Borle et al. 2005; Sloot, Fok, and Verhoef 2006).

inclined to buy the category in the channel that offers the largest
assortment.

Price Differences
Multichannel retailers can charge different prices in their

online and offline channel in view of cost and demand
considerations (Neslin and Shankar 2009; Wolk and Ebling
2010). For one, the online channel may entail higher operational
costs, including additional ICT, picking, handling and delivery
costs. At the same time, the online channel may experience cost
savings as the result of lower store layout, display and shelf
replenishment costs, and because price adjustments can literally
be executed by pressing a button. In addition, several studies
provided evidence of channel differences in price sensitivity
(Chu, Chintagunta, and Cebollada 2008; Wolk and Ebling 2010).
Multichannel grocery retailers can incorporate these cost and
price sensitivity differences in product prices to safeguard profit
margins (compensate for higher online operational costs) or to
stimulate online purchases (let consumers benefit from lower
online operational costs or use different price levels to exploit
price sensitivity differences). Similar to assortment differences,
we assume that multichannel shoppers will incorporate price
differences in their category allocation decisions, and allocate a
lower share of category purchases to the channel where the
category is least attractive in price (cf. multiple store shopping
literature; Gijsbrechts, Campo, and Nisol 2008; Vroegrijk,
Gijsbrechts, and Campo 2013).

Promotion Differences
The intensity of promotional actions can differ between the

online and offline channel of the same retailer to account
for differences in price/promotion sensitivity across channels
(Wolk and Ebling 2010) or for more pragmatic reasons such as

Image of Fig. 1

66 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

different account managers being in charge of the promotion
planning in each channel (Avery et al. 2012). Consumers can
react by temporarily adjusting their allocation patterns to take
advantage of more attractive promotional actions in one of both
channels.

In-store Stimuli
In-store stimuli may trigger a forgotten need or new idea.

Compared to the offline channel, online shoppers tend to be
less sensitive to these in-store stimuli for several reasons: they
can more easily control their shopping route and immediately
navigate to the needed category by clicking the category’s
page; they do not have to wait at fresh meat/fish counters or at
the cash register (locations that are often used to store impulse
products) and the more ‘functional’ online shopping environment
can evoke a more goal-oriented shopping attitude making
consumers more reluctant to deviate from their purchase plans
and give in to impulse purchases (Babin and Darden 1995). We
therefore expect that the online store will obtain a lower share of
purchases for impulse categories that consumers do not usually
plan in advance to buy and for which they tend to be very
sensitive towards in-store stimuli.

Transaction Utility: The Impact of Perceived Purchase Risk
and Shopping Convenience

Multichannel studies have indicated that channel differences
in transaction costs can depend on the categories that need to be
purchased and are mainly based on two components: (i) perceived
purchase risk and (ii) shopping convenience (Chintagunta, Chu,
and Cebollada 2012; Gupta and Kim 2010). Online purchases can
be associated with a higher perceived purchase risk as a result
of the products’ intangibility, i.e., the lack of sensory decision
cues (Degeratu, Rangaswamy, and Jianan 2000; Laroche et al.
2005). On the other hand, online shopping provides convenience
advantages through the possibility of having products picked up
by online grocery staff and having them delivered at home (Chu
et al. 2010; Gupta and Kim 2010).

Perceived Purchase Risk
The lack of sensory information in the online store can

constitute an important disadvantage for sensory categories –
such as fresh meat, vegetables and fruit – that tend to be evaluated
prior to purchase based on sensory information cues (Degeratu,
Rangaswamy, and Jianan 2000; Hoch 2002; Laroche et al. 2005;
Peck and Childers 2003). Not being able to see or touch products
can complicate the evaluation process and lead to greater
uncertainty and a higher perceived risk of online purchases
(Laroche et al. 2005; Pauwels et al. 2011; Weathers, Sharma, and
Wood 2007). This may increase the transaction costs of buying
sensory products in the online store (Gupta and Kim 2010) and
result in relatively lower online purchase shares of sensory
categories compared to other categories (Chintagunta, Chu, and
Cebollada 2012).

Shopping Convenience
The shopping convenience advantage of online stores may

especially benefit bulky and heavy categories since online
shopping eliminates the burden of physically handling these
products, e.g., putting them into the basket and carrying them
home. The resulting increase in transaction utility can lead to
disproportionately higher online category purchase shares of
bulky and heavy categories (Chintagunta, Chu, and Cebollada
2012).

Moderating Impact of Online Buying Experience

Because online shopping for groceries is lagging behind
compared to other categories (McPartlin and Dugal 2012),
many consumers are still relatively new to and unfamiliar with
the online grocery store environment and shopping process.
Consequently, they may adjust their purchase behavior as they
gain more experience with buying groceries online. For this
reason, and given that the online purchase tendency can differ
across grocery categories, we include category-specific online
buying experience as a moderator of category allocation
decisions. Based on the previous discussion and consumer
behavior literature, we postulate that experience can work in
different ways: (i) reduce the uncertainty and perceived risk of
online purchases (Frambach, Roest, and Krishnan 2007; Iyengar,
Ansari, and Gupta 2007; Kim, Ferrin, and Rao 2008), (ii) help
to gain additional factual and choice-related knowledge (Alba
and Hutchinson 1987; Iyengar, Ansari, and Gupta 2007) and
(iii) involve a learning process in which consumers adjust their
evaluation and decision processes to the new store environment
(Degeratu, Rangaswamy, and Jianan 2000; Hamilton and
Thompson 2007; Hoch 2002). Experience can thus attenuate as
well as reinforce category differences in online purchase share, or
it may not affect the allocation pattern at all when no risk is
involved or no learning process is needed.

We expect that experience has a mitigating effect on the
reluctance to buy sensory categories online. First, conditional
upon a positive and satisfying outcome, experience can enhance
confidence in the online purchase outcome and increase trust in
the retailer’s selection and delivery process (cf. Kim, Ferrin, and
Raghav Rao 2008; Urban, Amyx, and Lorenzon 2009). Second,
experience helps with ‘learning’ to infer missing information
from other – verbal and visual – cues that can be easily accessed
in the online store and that are diagnostic of the product’s quality
(e.g., quality labels, product characteristics that act as a quality
cue such as brand names and expiration dates) (Degeratu,
Rangaswamy, and Jianan 2000; Laroche et al. 2005; Peck and
Childers 2003).

On the other hand, we expect that consumers may not be
able to accurately assess assortment size and price differences
between the online and offline channel from the start. For low
involvement, multi-category purchases such as groceries,
consumers may not be able or motivated to go through a
complete evaluation of the entire assortment, and hence, may
not be fully aware of actual assortment or price differences.
After some online purchases in the category, they may
gradually become aware that some items are missing or only

67K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

available in the online assortment, or that some items are higher
or lower priced online (Alba and Hutchinson 1987; Hoyer and
MacInnis 2010). As a result, consumers may adjust their
channel preferences and purchase allocation pattern, and these
experience-based corrections in assortment and price percep-
tions may thus reinforce initial assortment and price effects.

Finally, we expect that sales promotions, in-store stimuli
and the convenience of buying bulky/heavy categories are
easy to evaluate without much online shopping experience.
Hence, there is no incentive to learn and adjust the shopping
process, and the reaction to these factors is expected to be
immediate and independent of a consumer’s online shopping
experience.

Model

To examine multichannel category allocation decisions, we
focus on the online channel’s share in category spending (SCS),
taking overall spending at the chain as given. Using a relative
instead of absolute measure of online category expenditures has
the advantage of removing the effect of customer and category
differences in total spending. In line with multiple store shopping
literature (Gijsbrechts, Campo, and Nisol 2008; Vroegrijk,
Gijsbrechts, and Campo 2013), we concentrate on allocation
patterns over a longer period of time (i.e., bi-weekly periods, t),
rather than category purchase decisions on a visit-by-visit basis.
In addition, because consumers only have to decide how to
allocate their purchases within this two-week period when they
plan to buy the category and when they visit both channels, we
focus on observations with (i) a category need (i.e., an online and/
or offline purchase in the category) and (ii) an online and offline
store visit (i.e., a multichannel shopping period where consumers
are in the opportunity to buy the product online and/or offline
and allocation is not pre-defined to 0% or 100%). This allows
us to eliminate both the effect of a consumer’s general online
buying tendency (decision to visit the online store) and the
effect of category purchase decisions on observed category
allocations.

The online channel’s share in spending for category c in
period t for household i (SCSit

c ) is defined and estimated over
all categories simultaneously (pooled estimation)2:

SCScit ¼
eU

c
it

1 þ eUcit : ð1Þ

By using a logistic model in Eq. (1), we ensure that the values
of the outcome variable are restricted within the zero–one range.
To linearize the model, we use the method of log-centering
(Cooper and Nakanishi 1996; Lesaffre, Rizopulos, and Tsonaka
2007), that has been applied in many other studies (see

2 To simplify the discussion, we use an overall index t and c for time periods
and categories respectively. As we only include multichannel purchase
occasions (periods where household i visited both channels), and categories
for which the household made a purchase within this period, the time index is
actually household-specific while the category index is household- plus time-
specific.

e.g., Cleeren, van Heerde, and Dekimpe 2013; Leenheer et al.
2007):

ln
SCScit

1−SCScit

� �
¼ Vcit þ μcit: ð2Þ

To avoid that the dependent variable in Eq. (2) is equal to the
log of zero (SCSit

c equal to zero) or an undefined value (division
by zero, SCSit

c equal to one), we add a small amount to the
numerator and denominator of Eq. (2) (cf. Bass et al. 2009;
Cleeren, van Heerde, and Dekimpe 2013), such that:

ln
SCScit þ 0:001

1−SCScit þ 0:001
� �

¼ Vcit þ μcit: ð2′Þ

We use consumer, marketing mix and experience as
explanatory variables:

Vcit þ μcit ¼ γ0i þ γ1 � STSit þ γ2 � Expcit þ γ3 � Usageci
� �
þ δ1 � Assc þ δ2 � Pricec þ δ3 � Promoc þ δ4 � ISSc½ �
þ δ5 � Sensc þ δ6 � Bulky Heavyc½ � þ μcit:

ð3Þ
The first square brackets in Eq. (3) capture consumer

characteristics that account for individual differences in the
tendency to allocate purchases in category c to the online channel,
including a consumer-, category- and time-specific online buying
experience variable (Expit

c), and a usage variable capturing the
consumer’s overall experience with the category (Usagei

c). In
addition, we include the online store’s share in total spending in
period t for consumer i (STSit), defined as the overall percentage
of online purchases in total grocery expenditures of consumer i at
the chain in period t. Including this variable allows capturing
category-specific deviations from the overall online/offline
allocation pattern that result from channel differences in
acquisition and transaction utility. The second square brackets
include variables that may entail channel differences in acquisition
utility, i.e., category-specific channel differences in assortment
size (Assc), price (Pricec), promotion (Promot

c), and in-store
stimuli (ISSc). The third square brackets capture the effect of
transaction cost related characteristics, including whether the
category is a sensory (Sensc), or bulky/heavy (Bulky_Heavyc)
category. We describe the operationalization of these variables in
the Data section. μit

c is a normally-distributed error term.
To incorporate the effect of category-specific online buying

experience, we use a model with varying coefficients (Foekens,
Leeflang, and Wittink 1999; Kopalle, Mela, and Marsh 1999).
The parameters of the category-specific variables are a function
of experience, allowing the effect to increase or decrease with
higher levels of online buying experience in the category:

δq ¼ δq0 þ δq1 � Expcit; q ¼ 1–6ð Þ: ð4Þ

Next, because channel differences in assortment and price
variables can be inspired by management expectations on
multichannel purchase behavior, we control for potential

68 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

endogeneity of these variables using a control function ap-
proach (Luan and Sudhir 2010; Petrin and Train 2010)3. Web
appendix A provides more detailed information. Our final model
includes the residuals of the control function models of assortment
(Res_Assc) and price (Res_Pricec) as additional variables:

Vcit ¼ γ0i þ γ1 � STSit þ γ2 � Expcit þ γ3 � Usageci
� �
þ ½δ1 � Assc þ δ2 � Pricec þ δ3 � Promoc þ δ4 � ISSc�
þ δ5 � Sensc þ δ6 � Bulky Heavyc½ �
þ ½θ1 � Res Assc þ θ2 � Res Pricec�:

ð3′Þ

Finally, to capture unobserved heterogeneity, we use (i) latent-
class estimation, allowing the parameters of explanatory variables
to vary across latent segments (Andrews, Ainslie, and Currim
2002; Kamakura and Russell 1989), and (ii) a random coefficient
approach by introducing a standard normally distributed latent
factor (Fi), allowing intercepts to vary across households
(Vermunt and Magidson 2013). We formulate the household-
specific intercept in Eq. (3′) as:

γ0i ¼ γ01 þ γ02 � Fi: ð5Þ
We use Latent GOLD® software to compute the latent factor

and estimate the coefficient γ02 (while fixing the value of the
standard deviation of the latent factor to 1; see Vermunt and
Magidson 2013, p 100–101). Latent GOLD® uses a factor-
analytic parameterization of the random-intercept model. The
parameter γ02 can be interpreted as the standard deviation of the
random intercept. The significance of the parameter gives an
indication of the importance of household differences in the share
they allocate to the online store. A non-significant parameter,
corresponding to a zero standard deviation of the intercept, points
to homogeneous online purchase tendencies.

The log-likelihood function defined by Eq. (3) and Eqs. (2′),
(3′), (4), and (5) is given by:

LL ¼
X

i
ln

X
s
Pi sð Þ∏t∏c f ln

SCScit þ 0:001
1−SCScit þ 0:001

� ��Vcit;s
� �

;

ð6Þ

where Vit,s
c is the segment-specific version of Eq. (3′) that allows

for differences between segments in their sensitivity to factors
that affect channel allocation decisions, f is the joint density

3 We expect that the endogeneity problem is especially important for the
assortment and price variables because these are typically long-term strategic
decisions where the offline channel’s price and assortment are taken into
account. Promotions, on the other hand, are expected not to have an
endogeneity problem because they are short-term decisions made independently
from the decisions in the other channel. Estimation of a control function model
for promotion intensity indeed provided extremely low explanatory value, and
robustness checks confirmed that no improvements in fit or substantive results
can be gained when controlling for endogeneity in the promotion variable.

function of the normal distribution and Pi(s) is the (a priori)
probability that household i belongs to segment s, which is
defined as:

Pi sð Þ ¼
eφsXR
r¼1e

φr
; ð7Þ

where φs reflects the size (importance) of segment s and R is the
total number of segments. Eq. (7) indicates that segments are
defined over a household’s complete purchase history, i.e., over
all time periods t and categories c.

Data

Our data come from a major European grocery chain which
has a prominent presence throughout the country and is one of
the leading offline and online grocery retailers. As we focus on
online and offline stores of a single retail chain, online and
offline assortments mainly differ in size and not in composition
(the online assortment is a subset of the offline assortment), and
category prices are directly comparable (price differences are
not linked to quality differences). When an online order gets
placed, professional shoppers (pickers) fill the order from an
independent warehouse; the retailer then delivers the order to
the place and at the time specified by the consumer. The online
store operates independently and is given full control over
merchandising decisions. As a consequence and notwithstanding
the similarities in chain policy, there are differences between the
online and offline channel in assortment size, product prices and
promotional actions.

We used loyalty card information to link online and offline
purchase data over a one-year period (2006). To get stable
model estimations and a representative sample of multichannel
shoppers, we focus on households that made (i) at least two
online and two offline store visits during the estimation period
(thereby excluding one-off online trial purchases), and (ii) at
least two purchases in the category (irrespective of the channel,
to include heavy as well as light buyers of the category). In the
model estimations, we made a further selection and only focus
on bi-weekly periods (of retained households) with a visit to
both channels and a category purchase in at least one of the
channels. During these periods, the household needs the
category, but allocation is not predetermined as would be the
case in online-only (100% online) or offline-only (0% online)
periods. Table 1 gives an overview of the 25 frequently-purchased
categories that were used, and indicates per category the number
of households and observations retained.

As Table 1 indicates, most categories that we examined are
purchased during multichannel shopping occasions on a regular
basis: on average 32% of all transactions are multichannel
transactions (min. 24% for vegetables and max. 40% for water).
Online-only shopping occasions occur least often (on average
12%; min. 1% for fresh fish and max. 20% for water), while
offline-only shopping occasions are most common (on average
57%; min. 40% for water and max. 70% fresh fish). In general,
consumers are more likely to visit (and purchase categories in)
the offline channel than the online channel: the average number

Table 1
Descriptives across the 25 categories.

Category # of HH retained that # of bi-weekly periods with category purchase
at (% of total transactions with category purchase)

Purchase frequency
(average # of bi-weeks/
year with cat. purch.)

Online category share of spending

Purchase cat. ≥2 times
and have ≥1 multichannel
transaction

Both channels
(multichannel
transactions)

Online
channel only

Offline
channel only

Online Offline Across bi-weeks
with cat. purch.

Across bi-weeks
with cat. purch.
& MC trans.

Fresh meat 421 1,215 (33.27%) 429 (11.75%) 2,008 (54.98%) 3.90 7.66 .27 .41
Charcuterie 572 2,147 (25.96%) 754 (9.12%) 5,371 (64.93%) 5.07 13.14 .20 .34
Fresh fish 385 1,102 (28.77%) 55 (1.44%) 2,673 (69.79%) 3.01 9.81 .05 .08
Fruit 567 2,020 (26.82%) 726 (9.64%) 4,786 (63.54%) 4.84 12.00 .23 .39
Vegetables 640 2,572 (24.42%) 879 (8.35%) 7,081 (67.23%) 5.39 15.08 .17 .27
Bakery pastry 581 2,112 (25.62%) 561 (6.81%) 5,569 (67.57%) 4.60 13.22 .12 .20
Fat 520 1,728 (30.62%) 718 (12.72%) 3,197 (56.65%) 4.70 9.47 .33 .54
Cheese 624 2,427 (26.14%) 1,076 (11.59%) 5,782 (62.27%) 5.61 13.16 .26 .42
Milk 580 2,169 (34.60%) 1,011 (16.13%) 3,088 (49.27%) 5.48 9.06 .37 .64
Yoghurt 590 2,299 (25.96%) 916 (10.34%) 5,641 (63.70%) 5.45 13.46 .23 .37
Canned fruit & veg. 525 1,716 (35.41%) 670 (13.83%) 2,460 (50.76%) 4.54 7.95 .41 .62
Condiments & sauces 484 1,411 (32.90%) 481 (11.21%) 2,397 (55.89%) 3.91 7.87 .30 .46
Breakfast cereals 362 1,064 (32.86%) 349 (10.78%) 1,825 (56.36%) 3.90 7.98 .37 .59
Biscuits 515 1,758 (29.42%) 634 (10.61%) 3,583 (59.97%) 4.64 10.37 .26 .43
Pastes & rice 495 1,443 (34.03%) 535 (12.62%) 2,262 (53.35%) 4.00 7.48 .37 .56
Chocolate 437 1,283 (29.86%) 398 (9.26%) 2,616 (60.88%) 3.85 8.92 .23 .37
Hot beverages 505 1,794 (32.90%) 759 (13.92%) 2,900 (53.18%) 5.06 9.30 .37 .55
Water 603 2,413 (39.80%) 1,231 (20.30%) 2,419 (39.90%) 6.04 8.01 .56 .84
Juice 408 1,258 (35.92%) 411 (11.74%) 1,833 (52.34%) 4.09 7.58 .42 .65
Soft drinks 504 1,768 (34.00%) 813 (15.63%) 2,619 (50.37%) 5.12 8.70 .49 .73
Pet food 237 900 (34.19%) 455 (17.29%) 1,277 (48.52%) 5.72 9.19 .47 .67
General body care 511 1,608 (31.63%) 535 (10.53%) 2,940 (57.84%) 4.19 8.90 .28 .48
Washing products 538 1,713 (39.13%) 687 (15.69%) 1,978 (45.18%) 4.46 6.86 .54 .77
Toilet paper 522 1,707 (38.00%) 627 (13.96%) 2,158 (48.04%) 4.47 7.40 .49 .73
Cleaning products 578 1,940 (30.56%) 686 (10.81%) 3,722 (58.63%) 4.54 9.80 .38 .60
Average (across 25 cat.) 31.71% 11.84% 56.45% 4.66 9.69 .33 .51

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Table 2
Variable notation & description.

Notation Name Description Formula

SCSit
c Share in category spending of

consumer i for category c in period t
Online spending in category c by customer i in period t
(Spendingit

online,c) divided by overall spending (online and
offline: Spendingit

online,c + Spendingit
offline,c) in category c for

consumer i in period t. (online and offline prices are measured
in constant prices; period t are bi-weekly periods where the
consumer visited the online and offline store and made a
purchase in the category in the online and/or offline store).

SCScit ¼
Spendingonline;cit

Spendingonline;cit þ Spending
offline;c
itð Þ

STSit Share in total spending of consumer i
in period t

Online spending across all categories by customer i in period t
(Spendingit

online) divided by the overall grocery spending (online and
offline: Spendingit

online + Spendingit
offline) for consumer i in period t.

STSit ¼ Spending
online
it

Spendingonlineit þ Spending
offline
itð Þ

Assc Assortment difference for category
c

Assortment difference ratio (number of SKUs in category c in
the online store divided by the number of SKUs in category c in
the offline store).

Assc ¼ Assonline;c
Assoffline;c

Pricec Price difference for category c The unit price difference (difference between online and offline
average unit prices computed over a common set of category
products, i.e., the set of products that are available in both
channels).

Pricec = Priceonline,c − Priceoffline,c

Promot
c Online share in promotion intensity

for category c in period t
‘Share-of-voice’ based variable, measured as the share in
overall category promotions of the online store (number of
SKUs on promotion in category c in the online store in period t,
divided by the number of SKUs on promotion in category c in
the online and offline store combined in period t; equal to 0 in
case there were no promotions in the category).

Promoct ¼
NrPromoonline; ct

NrPromoonline; ct þ NrPromooffline; ct

ISSc In-store stimuli dummy variable
for category c

Indicator variable equal to 1 if sensitivity towards in-store
stimuli is high for category c, 0 elsewhere.

Sensc Sensory dummy variable for
category c

Indicator variable equal to 1 if category c is a sensory category,
0 elsewhere.

Bulky_Heavyc Bulky/heavy dummy variable for
category c

Indicator variable equal to 1 if category c is a bulky or heavy
item category, 0 elsewhere.

Expit
c Online buying experience of

consumer i for category c in period t
Weighted sum of previous online purchases in category c for
consumer i in period t (bi,t − 1

c ), with weights equal to λ
(between 0 and 1) and based on all the previous periods (s = 1, …,
t − 1) to capture fading effects, and Expi1

c as starting value based
on an initialization period of 26 bi-weeks (we used λ = .7 and
checked the results’ sensitivity via robustness checks).

Expcit ¼ λ � Expci;t−1 þ λ � bci;t−1 ¼
∑s¼t−1s¼1 λ

s � bci;t−s þ λt−1 � Expci;1

Usagei
c Online usage level of consumer i

for category c
Indicator variable of whether consumer i is a heavy user of
category c based on the estimation period.

4 We have detailed offline assortment and price information for one time
period only and therefore had to use time-independent price and assortment
variables. However, for the retailer under consideration, regular price and
assortment within a category hardly changed during our observation period. In
addition, we only have category-level data and are constrained in making
marketing mix variables individual-specific (e.g., by using SKU-weights).
5 We explicitly checked whether price differences between the online and

offline channel were related to the online assortment reduction strategy (e.g.,
only the more expensive items in the online assortment) and found that this was
not the case since the online assortment of all categories covers a range of items
with different price levels.

70 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

of bi-weeks per year with a purchase in the category equals 4.66
for the online channel and 9.69 for the offline channel. The
online category share of spending equals 33% (min. 5% for
fresh fish and max. 56% for water) across all bi-weekly periods
with a category purchase and increases to 51% (min. 8% for
fresh fish and max. 84% for water) for the multichannel periods
only.

Table 2 describes the details of the variable operationalization.
The share in category spending is operationalized as the ratio of
online purchases in category c by consumer i in period t, divided
by the consumer’s overall category purchases during that period
in the online and offline channel combined. As we focus on
multichannel shopping occasions, consumers may distribute
purchases over both channels (share of online category spending
between zero and one), but they can also decide to allocate the
purchases to one of both channels (share of online category
spending equal to zero or one).

Marketing mix information was obtained via the retailer. As
a measure of channel differences in assortment size, we used
the ratio of the assortment size (number of SKUs) of category c
in the online store divided by the assortment size (number

of SKUs) of category c in the offline store4. This ratio is
comparable across categories, and is smaller (larger) than one
when the online assortment is smaller (larger) than the offline
assortment. To capture the category price variable, we compute
the difference in average category prices between online and
offline stores (average price for the set of category products that
is available in both channels)5. To capture promotion effects,
we use the share in overall category promotions of the online
store, defined as the number of SKUs on promotion in category
c at time t in the online store, divided by the number of SKUs

71K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

on promotion in category c at time t in the online and offline
store combined. This eliminates the effect of differences in
assortment size and makes the variable comparable across product
categories. The in-store stimuli variable is operationalized as a
dummy variable that is equal to one when purchases of category c
are often unplanned and strongly influenced by in-store stimuli.
This classification was checked by survey data, where a
representative convenience sample of respondents assessed on a
7-point Likert scale the extent to which a category is bought
spontaneously when seeing it in the store (t = −7.41, p b .01).
The categories that were classified as ‘high in-store sensitive’
match those where the majority of the respondents indicated they
often buy these categories without having planned the purchase.
Sensory and bulky/heavy characteristics are captured by dummy
variables equal to one when the category is classified as sensory
or bulky/heavy. Like for the in-store stimuli variable, we checked
the sensory classification with survey data, where a representative
convenience sample of respondents was asked to rate each
category on the importance of physical inspection of sensory
attributes prior to purchase (t = −15.684, p b .001). Bulky/heavy
categories are categories for which more than 75% of online
shoppers in our dataset buy package sizes that exceed a certain
weight (e.g., multi-packs) or that are considered as bulky
according to management.

To capture online buying experience, we use the weighted
sum of previous online purchases in the category (cf. Foekens,
Leeflang, and Wittink 1999), and use an initialization period of
26 bi-weeks to compute the starting value6. The experience
variable increases with the number of previous purchases
(frequency effect), but each previous purchase receives a weight
that becomes smaller when the purchase occurred longer ago
(recency effect) (see Table 2). The resulting experience measure is
larger when the customer has purchased the category more often
and more recently in the online store, and varies substantially
across households and over time (range = [0, 2.33], mean = .46,
standard deviation = .58). Finally, category-specific usage is
operationalized as the average spending of consumer i in category
c divided by the global average for category c, to make the
variable comparable across categories.

Table 3 classifies the categories according to marketing mix
differences and sensory, heavy/bulky and impulse characteristics.
The classification clearly shows that there is sufficient variation
across the different characteristics. On average, online assort-
ments tend to be smaller while online prices tend to be higher.
Several other online grocery chains follow a similar strategy
(Cheng 2010). The degree of assortment reduction and the size of
the online price premium, however, substantially differ across
categories.

Empirical Results

Estimation results of the control function models can be found
in Web Appendix A. We estimated the endogeneity-corrected

6 We have one year of data (2006) on online and offline category purchases
that allows us to derive multichannel occasions. But, we have one additional
year (2005) of online data that allows us to initialize the experience variable.

version of the SCS model with a varying number of latent classes.
Although additional segments provide a further improvement in
goodness-of-fit, there is a clear elbow (Fig. 2) in the graph of the
Bayesian Information Criteria (BIC) statistic at four segments
with additional segments providing only a minor improvement in
fit. The BIC statistic also indicates that the correction for
endogeneity improves the results (BIC of four-segment model
without vs. with endogeneity correction: 253,836 vs. 253,828).
Overall the model explains the differences in allocation pattern
across categories and consumers very well (pseudo R2 = .48). To
investigate to what extent product category characteristics,
experience effects and household characteristics contribute to
the model’s explanatory power, we examined the variance
decomposition. Results of partial model estimations indicate that
each of these explanatory variables significantly improves
goodness-of-fit, both based on Radj

2 and likelihood ratio statistics.
The increase in Radj

2 (LR statistic) for instance, amounts to .18
(LR = 10,574; p b .005) for product characteristics (compared to
an intercepts-only model), to .04 (LR = 2,888, p b .005) for
experience (compared to a model with intercepts and product
characteristics) and to .08 (LR = 5,594, p b .005) for household
characteristics other than experience (compared to a model with
intercepts, product characteristics and experience effects). We
also conducted several robustness checks to verify the validity of
our model and the consistency of our findings. They are
summarized in Web Appendix B. Table 4, Panel A reports the
estimation results for the homogeneous model as well as for the
four-segment model. As we will focus on the results of the
four-segment model, we first describe the differences across
segments, and next provide a general discussion of the main and
interaction (experience) effects.

Overall, in terms of segment differences, we find that
segment 1 customers (29% of all customers) are most sensitive
to purchase allocation factors (assortment, promotion, in-store
stimuli, sensory and bulky/heavy), and make the strongest (effect-
reducing) adjustments when they gain more online buying
experience. Segment 2 customers (22%) are sensitive to price
differences, in-store stimuli, sensory and bulky/heavy allocation
factors, but are less sensitive to experience effects than segment 1,
which can be explained by the low overall increase in online
buying experience (see below). Segments 3 and 4 (14% and 35%
of the customers respectively) are both much less sensitive to the
examined allocation factors than customers of the other segments
(significant effects are limited to in-store stimuli and bulky/
heavy), but differ between each other in online buying experience
reactions. While higher levels of experience have almost no effect
on segment 3 consumers, segment 4 customers adjust their
reaction to channel price differences, in-store stimuli, sensory and
bulky/heavy categories in a positive way.

We thus observe differences between consumer segments in
online buying experience effects: (i) attenuating effects that
reduce category differences in purchase allocation (segments 1
and 4), (ii) reinforcing effects that increase category differences
in purchase allocations (segment 2), and (iii) no or limited
adjustment effects (segment 3). Table 4, Panel B provides an
overview of segment characteristics that can explain these
differences in reactions. Segments 1 and 4 both allocate a large

Table 3
Classification of 25 categories.

Category Assortment reduction (low/high) a Price difference (low/high) b Impulse (yes/no) Sensory (yes/no) Bulky/heavy (yes/no)

Fresh meat High High No Yes No
Charcuterie High High No Yes No
Fresh fish High High No Yes No
Fruit Low Low No Yes No
Vegetables High Low No Yes No
Bakery pastry High Low Yes Yes No
Fat Low Low No No No
Cheese High High No Yes No
Milk Low Low No No No
Yoghurt High Low No No No
Canned fruit & vegetables High High No No No
Condiments & sauces Low High No No No
Breakfast cereals Low High No No No
Biscuits High High Yes No No
Pastes & rice High High No No No
Chocolate Low High Yes No No
Hot beverages Low High No No No
Water Low Low No No Yes
Juice Low Low No No No
Soft drinks Low Low No No Yes
Pet food Low High No No No
General body care High High No No No
Washing products Low Low No No No
Toilet paper High Low No No Yes
Cleaning products Low High No No No

a The low and high assortment reduction cover the range of .461–.614 and .060–.459, respectively.
b The low and high price difference cover the range of .000–.289 and .320–2.000, respectively.

72 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

share of purchases to the online channel, and their level of online
buying experience increases substantially over the estimation
period. In addition, segment 4 already had a relatively high level
of experience at the start, which can explain the smaller number
of significant main effects (the experience-reducing effects have
to some extent already taken place). We label segment 1 as ‘new
online grocery fans’ and segment 4 as ‘experienced online
grocery fans’. Compared to segments 1 and 4, segments 2 and 3
both allocate a low(er) share to the online channel in general,
which may explain the absence of experience-reducing effects. In
contrast to segment 3, segment 2 customers’ online experience
level remains low, which may additionally signal a low interest in
the online channel and thus could explain experience-reinforcing

Fig. 2. Model goodness-of-fit.

effects. We label segment 2 as ‘online grocery skeptics’ and
segment 3 as ‘occasional online grocery shoppers’.

In terms of model estimation results, we find that the latent
factor coefficient is significant for all segments, indicating that
there is still some ‘unobserved’ (unexplained) variation across
households in the overall tendency to spend a larger SCS online.
However, comparison of the magnitude of this coefficient (which
captures the standard deviation of the intercept over households;
see Model section) with that of the segment-specific intercept
(i.e., the constant which captures the average effect) indicates that
the model explains a large part of the (observed) household
variation in online buying tendency. We further obtain significant
and expected positive effects for the control variables, share in
total spending (STSit) and experience (Expit

c), across all four
segments. The category usage level (Usaget

c), on the other hand,
is negative and significant for two out of four segments. A
possible explanation for this negative effect could be that heavy
users, who buy the category more frequently, buy a lower share
online because they have more opportunities to buy the category
in the offline store.

In terms of the impact of acquisition utility factors, we find
that assortment differences have a weakly significant and positive
main effect in one segment (segment 1, δ10,s1 = 6.710, p b .10),
and a significant and positive experience interaction effect for
two other segments (segment 2: δ11,s2 = 5.439, p b .10; segment
3: δ11,s3 = 1.729, p b .01). These results indicate that the online
channel captures a larger share of category purchases in
categories where the online assortment is more similar in size to
the offline assortment for 3 out of the 4 segments (65% of the
consumers), but for some customers (segments 2 and 3, 36%)

Image of Fig. 2

Table 4
Model estimation results.

Variables Homog. model Four-segment model

Seg. 1 Seg. 2 Seg. 3 Seg. 4

Panel A: Parameter coefficients
Constant (γ01) −5.654 ⁎⁎⁎ −5.518 ⁎⁎⁎ −3.877 ⁎⁎ −4.796 ⁎⁎ −6.914 ⁎⁎⁎
Latent factor (γ02) −1.048 ⁎⁎⁎ .772 ⁎⁎⁎ .670 ⁎⁎⁎ −1.144 ⁎⁎⁎ .573 ⁎⁎⁎
Share in total spending (γ1) 9.680 ⁎⁎⁎ 7.901 ⁎⁎⁎ 6.862 ⁎⁎⁎ 10.316 ⁎⁎⁎ 10.955 ⁎⁎⁎

Experience (γ2) 2.232 ⁎⁎⁎ 1.846 ⁎⁎⁎ 10.535 ⁎⁎⁎ 3.441 ⁎⁎⁎ .711 ⁎⁎

Usage level (γ3) −.239 ⁎⁎⁎ −.046 −.054 −.389 ⁎⁎⁎ −.298 ⁎⁎⁎

Acquisition utility (marketing mix)
Assortment (δ10) 3.297 ⁎ 6.710 ⁎ −.887 −3.136 5.782
Assortment ⁎ experience (δ11) 1.006 ⁎⁎⁎ .738 5.439 ⁎ 1.729 ⁎⁎ −.159
Price (δ20) −.270 ⁎⁎ −.074 −1.564 ⁎⁎⁎ .084 −.087
Price ⁎ experience (δ21) .463 ⁎⁎⁎ 1.041 ⁎⁎⁎ −.953 −.247 1.050 ⁎⁎⁎
Promotion (δ30) .270 ⁎ .737 ⁎⁎ −.031 .306 .096
Promotion ⁎ experience (δ31) −.365 ⁎⁎ −.635 .621 −.276 −.388
In-store stimuli (δ40) −1.445 ⁎⁎⁎ −.606 ⁎⁎ −1.468 ⁎⁎⁎ −1.124 ⁎⁎⁎ −1.881 ⁎⁎
In-store stimuli ⁎ experience (δ41) .155 −.650 ⁎ −3.092 ⁎⁎⁎ −.078 .367 ⁎

Transaction utility (category characteristics)
Sensory (δ50) −2.717 ⁎⁎⁎ −4.527 ⁎⁎⁎ −3.379 ⁎⁎⁎ −1.695 ⁎ −.364
Sensory ⁎ experience (δ51) .964 ⁎⁎⁎ 2.054 ⁎⁎⁎ −1.253 −.519 ⁎⁎ .470 ⁎⁎
Bulky/heavy (δ60) 2.867 ⁎⁎⁎ 2.118 ⁎⁎⁎ 5.212 ⁎⁎⁎ 4.156 ⁎⁎⁎ .681 ⁎⁎

Bulky/heavy ⁎ experience (δ61) −.855 ⁎⁎⁎ −.848 ⁎⁎⁎ −9.724 ⁎⁎⁎ −.762 ⁎⁎⁎ .552 ⁎⁎⁎
Residual assortment (θ1) 2.023 −.195 4.682 7.085 ⁎ −.078
Residual price (θ2) −.326 ⁎⁎ −.194 1.569 ⁎⁎⁎ −.763 ⁎⁎ −1.316 ⁎⁎⁎
Segment membership (φs) 29% 22% 14% 35%
BIC 256,333.8 253,828.3

Panel B: Segment characteristics

Variables Four-segment model

Seg. 1 Seg. 2 Seg. 3 Seg. 4

Average online buying exp first 4 bi-weeks .339 .128 .394 .451
Average online buying exp last 6 bi-weeks .490 .191 .668 .837
Change in average online buying exp .151 .063 .274 .386
Total online spending amount (€) 932.69 401.47 1,041.25 1,677.81
Total offline spending amount (€) 1,505.21 2,825.98 3,554.61 1,718.19
Average online purchase share (%) 72.64 46.01 42.16 78.83

⁎ Significant at p b .10.
⁎⁎ Significant at p b .05.
⁎⁎⁎ Significant at p b .01.

73K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

only after they gain more online buying experience. To assess the
overall effect of assortment differences, these results have to be
evaluated in combination with the endogeneity correction effects.
The coefficient of the assortment control function residuals is
only significant at 10% for segment 3, indicating that there is
no serious endogeneity problem for the assortment variable
(Wooldridge 2013). Overall, these results indicate that consumers
are sensitive to assortment differences (except for segment 4), and
that actual differences in online and offline assortments are still
mainly guided by other managerial considerations than expected
customer reactions (no substantial endogeneity effect).

For price differences, we find a negative and significant effect
for the online grocery skeptic segment 2 (δ20,s2 = −1.564,
p b .01), and no significant effect for the other three segments.
Yet, in contrast to assortment, we obtain significant effects for the
residual of the price correction function in all segments except
segment 1. This indicates not only that the price variable is

endogenous, but also that the online-offline price differences are
in line with category differences in price sensitivity. This is
confirmed by the results of a model without endogeneity
correction, where price effects are negative and significant for
three out of four segments. The moderating effect of experience
is – contrary to our expectations – positive and significant for
the online grocery fan segments 1 and 4 (δ21,s1 = 1.041, p b .01;
δ21,s4 = 1.050, p b .01) and not significant for the other two
segments. So, while the price sensitivity of the online grocery
skeptic segment 2 consumers does not change their allocation
pattern when they gain additional experience (and online price
knowledge), consumers of online grocery fan segments 1 and 4
tend to adjust their spending levels to channel price differences in
an upward way (i.e., they increase the online share for categories
with larger online price premiums).

Promotions do not lead to higher spending levels in the
category except for the new online grocery fan segment 1

74 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

(δ30,s1 = .737, p b .01), who may pay more attention to online
promotional stimuli than online grocery skeptics or experienced
online grocery fans. This is also in line with previous
observations that – in general – promotions predominantly
affect brand choices, and have a much smaller or no effect
on category demand and store choices (Bell, Chian, and
Padmanabhan 1999). As expected, the effect does not change
with higher levels of online buying experience as none of the
interactions with experience are significant.

Categories for which in-store stimuli are important, are
purchased less easily in online stores as indicated by the
negative and significant effect on SCS decisions in each of the
segments. For the occasional online grocery shopper segment
3, this effect does not change with higher levels of experience
(they already adapted their allocation patterns prior to the
estimation period) while experience reinforces the negative
effect for the online grocery skeptic segment 2 (δ41,s2 =
−3.092, p b .01). For online grocery fan segments 1 and 4,
the effects are only marginally significant and very small
(segment 1: δ41,s1 = −.650, p b .10; segment 4: δ41,s4 = −.367,
p b .10). Overall, experience thus appears to have a negligible
effect on the sensitivity to in-store stimuli.

In terms of the impact of transaction utility factors, the
results provide support for the assumption that consumers will
allocate a relatively low share of sensory category purchases to
the online store: three out of four segments have a significant
negative effect for sensory categories (δ50,s1 = −4.527, p b .01;
δ50,s2 = −3.379, p b .01; δ50,s3 = −1.695, p b .10), and not for
the experienced online grocery fan segment 4. Experience has,
as expected, a positive effect on the share of sensory purchases
allocated to the online store for online grocery fan segments 1
and 4 (δ51,s1 = 2.054, p b .01; δ51,s4 = .47, p b .05). Experi-
ence has no effect on the online share in sensory purchases for
the online grocery skeptic segment 2, and a negative reinforcing
effect for the occasional online grocery shopper segment 3
(δ51,s3 = −0.519, p b .05), possibly as a result of negative
experiences with online sensory purchases.

In line with its shopping convenience benefit, the online store
attracts a relatively larger share of bulky and heavy category
purchases for all segments (δ60,s1 = 2.118, p b .01; δ60,s2 =
5.212, p b .01; δ60,s3 = 4.156, p b .01; δ60,s4 = .681, p b .01). In
contrast to our expectations, however, the effect weakens in three
out of four segments (δ61,s1 = −.848, p b .01; δ61,s2 = −9.724,
p b .01; δ61,s3 = −.762, p b .01) and strengthens in the other
segment (δ61,s4 = .552, p b .01). Consumers of the experienced
online grocery fan segment 4 that were somewhat more con-
servative at the start (smaller magnitude of main effect for bulky/
heavy) appreciate the shopping convenience benefit more and
more over time. Consumers of the other segments that were more
convinced about the shopping convenience benefit at the start
(larger magnitude of main effect for bulky/heavy) gradually
lower the online share of bulky and heavy categories. While the
convenience effect of heavy/bulky categories remains positive
and significant for all segments, the difference in effect across
segments becomes smaller as consumers gain more experience
with buying these categories online, but still varies substantially
across the four segments.

Discussion and Conclusions

The objectives of this research were twofold. First, we
wanted to provide a comprehensive analysis of the factors that
affect purchase allocation decisions of multichannel grocery
shoppers, thereby controlling for potential endogeneity biases
in marketing mix effects. Second, we wanted to investigate the
effect of online buying experience and test whether and for which
factors experience can have an online purchase enhancing or
rather reducing effect.

Factors of Multichannel Purchase Allocation Decisions

The results confirm that acquisition and transaction utility
based factors can influence the share of category purchases that is
allocated to the online store. The large majority of multichannel
shoppers (65%) is less inclined to buy categories online for which
the online store offers a less attractive (smaller) assortment.
Channel differences in price and promotion intensity have
respectively a negative and positive effect on a smaller subset
of multichannel shoppers (22% price, 29% promotion). All
consumers are less sensitive to in-store incentives and buy
substantially less impulse categories in the online channel
compared to the overall allocation of grocery purchases to the
online store. In addition to these traditional allocation factors, we
find significant effects of intrinsic category characteristics that
affect online transaction utility. As expected, the majority of
consumers (65%) is less inclined to buy sensory products online
because of the higher perceived online purchase risk and all
consumers purchase substantially more heavy/bulky products to
take advantage of online convenience benefits.

The Moderating Effect of Category-specific Online Buying
Experience

Previous research on general online purchase barriers has
stressed the positive impact of online experience in reducing
the resistance to buy online caused by factors such as the
financial risk of online transactions (Frambach, Roest, and
Krishnan 2007; Iyengar, Ansari, and Gupta 2007; Kim, Ferrin,
and Raghav Rao 2008). We observe a similar attenuating effect
of category-specific online buying experience for risk related
category characteristics. The negative effect of a lack of sensory
information gradually disappears for about 30% of the multi-
channel shoppers (‘new online grocery fans’), when they gain
more experience with buying sensory categories online and get
accustomed to selecting these products without prior physical
inspection.

Yet, in contrast to what has been found for online buying
experience in general, we show that more experience may also
lead to adverse effects for marketing mix based differences in
acquisition utility between both channels. Given the customers’
low involvement with grocery purchases and high time pressure
during a multi-category shopping task, they are often not
prepared to engage in complex evaluations such as detailed
comparisons of online–offline assortments. Instead, consumers
gain a better insight into actual assortment differences through

75K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

an experience-based learning process. As a result, more than
one third of the respondents (‘online grocery skeptics’ and
‘occasional online grocery shoppers’) gradually reduce their
online purchases of categories with a smaller online assortment
in favor of the offline channel as they become more clearly
aware of the restrictions in choice variety. Channel differences
in price also have a stronger impact on allocation patterns for
some consumers when they gain experience, but in contrast to
our expectations, the interaction effect with experience is
positive (larger share of category spending for categories with a
higher online price) for ‘new’ and ‘experienced online grocery
fans’. The results of the endogeneity correction indicate that for
segment 4 (‘experienced online grocery fans’), management has
anticipated channel differences in the online willingness-to-pay
correctly (significant price residual coefficient). The results for
segment 1 (‘new online grocery fans’) suggest that these
consumers are more quality-oriented (e.g., strong positive main
effect of assortment) and not very sensitive to price (no significant
main or endogeneity correction effect). This can explain the lack
of a negative effect of experience on online purchase shares of
categories with a larger price difference.

As expected, we did not find any moderating effect of
experience on the reaction to channel differences in promotion
intensity which are easy to evaluate from the start and do not
require any learning and adjustment process. While we expected
a similar (non-significant) effect for impulse purchases triggered
by in-store stimuli and online shopping convenience advantages
of heavy/bulky categories, experience has a negative effect
(marginally significant) for 51% of the consumers on impulse
purchases and 65% for heavy/bulky categories. For impulse
purchases, this can probably be explained by the fact that
consumers unfamiliar with the online grocery shopping environ-
ment have to search more to find the needed products which
increases their exposure to in-store stimuli. For heavy/bulky
categories, experience attenuates the allocation effect for most
consumers, but reinforces it for those who initially made less use
of the online convenience advantage. As a result, the difference
across consumer segments becomes smaller, but the effect
remains significant and positive for all consumers.

In terms of differences across consumers, results show that
there are clear differences in how segments change allocation
patterns when gaining more experience. Segments that are
enthusiastic about online shopping and its benefits (new and
experienced online grocery fans) are more likely to show
attenuating effects that reduce category differences in purchase
allocation. Segments that use the online store less frequently
(online grocery skeptics and occasional online grocery shoppers)
are less likely to adjust allocation over time and can even face
reinforcing effects that increase category differences in alloca-
tions when their experience level remains low.

Managerial Implications

Grocery retailers increasingly recognize the importance of
online stores to retain the existing customer base and nowadays
most of the large chains have opened an online store next to their
traditional offline supermarkets. By offering an additional

distribution channel that complements offline stores and offers
unique benefits such as greater accessibility and more convenience
and time saving (Chu et al. 2010; Gupta and Kim 2010), they
hope to increase their value proposition and gain a competitive
advantage over single-channel retailers (Chintagunta, Chu, and
Cebollada 2012; Kabadayi, Eyuboglu, and Thomas 2007; Zhang
et al. 2010). Yet, to assess and improve the profitability of the
multichannel strategy, retailers not only need to understand
whether and why customers will adopt the new online channel,
but also which share of the shopping baskets the online store can
attract to cover its relatively high operational costs. Our findings
contribute to a better understanding of the factors underlying
category differences in online performance and may in this way
help to define appropriate promotional and corrective actions that
can be taken to stimulate online purchases of less successful
categories.

A first important insight that can be derived from our
findings is that different actions may be needed to stimulate
online purchases. For marketing mix related factors, retailers
should realize that multichannel shoppers may react negatively
to excessive online assortment reductions, especially when they
gain more online buying experience. Large assortment
reductions can then have an important negative effect, implying
that online retailers may have to invest in upgrading online
assortments to better match the offline product offer. Online
shoppers are, on the other hand, more willing to tolerate online
price premiums when they gain more online buying experience
(and are thus better able to appreciate the online shopping
advantages). Nevertheless, for a substantial segment of con-
sumers (about 22%), high online price premiums do significantly
reduce the attractiveness of the online offer. While experience
does not reinforce this effect as we expected, it does not attenuate
it either.

The lower sensitivity to in-store incentives in the online
environment calls for promotional tactics that are better tailored
to the specific online environment, e.g., personalized promotions,
cross-selling opportunities, tailored in-store displays (Bellman
et al. 2013; Breugelmans and Campo 2011; Punj 2011) and that
may stimulate purchases of impulse categories in the online
channel. In addition, marketing communication can play an
important role in reducing the perceived risk and uncertainty of
online purchases and help customers to adjust decision rules to
the new shopping environment (Weathers, Sharma, and Wood
2007). Retailers can, for instance, use customer reviews or other
electronic word-of-mouth to highlight the positive experiences of
other shoppers with buying sensory categories online (Jiménez
and Mendoza 2013; Purnawirawan, De Pelsmacker, and Dens
2012). They can also help consumers by providing substitute
information cues (such as expiration dates and quality labels)
and by clarifying their usefulness in judging the product quality
of sensory categories. Lastly, retailers can stress the online
convenience benefits in their marketing communications to
further spur the higher tendency of buying heavy/bulky products
in the online channel.

A second important finding is that experience can have a
positive as well as a negative effect on the tendency to allocate
purchases of specific categories to the online channel. Results

76 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

show, for instance, that an increase in experience can strengthen
the negative effect of assortment differences. This points to
potential limitations for retailers when using assortment signaling
strategies. While less visible assortment reductions (eliminating
less popular items) may initially mask the less attractive online
offer, increased experience with buying the categories online may
improve the customer’s assortment knowledge and may result in
a stronger negative effect on online category allocations. On the
other hand, experience may reduce the perceived risk of buying
sensory categories online and thereby enhance online purchases
of these categories. Hence, retailers should strive to enhance
positive experiences by stimulating trial and repeat purchases for
sensory categories as it offers opportunities to reduce online
purchase risk.

Lastly, our findings indicate that there are clear differences
in how segments adjust their allocation pattern as they gain
more online buying experience. For the segment of frequent
online buyers, who are also more willing and open to buy
several types of categories in the online store, special loyalty
programs could be developed to maintain and reinforce their
use of the online channel. For the group of customers that spend
a smaller share of grocery products in the online channel and
who limit their online purchases to a more restrictive, ‘safer’ set
of categories, extension of online purchases could be aimed for,
for instance, by stimulating trial purchases of categories with a
higher perceived online buying risk (e.g., sensory categories). In
this way, these consumers experience (free or with promotion)
the positive outcomes of more risky purchases in the online
channel, which may help in developing trust in the multichannel
retailer’s ability to provide a high-quality online service (Urban,
Amyx, and Lorenzon 2009).

Directions for Further Research

Although our study provides interesting new insights into the
effect of multichannel category allocation factors and the
moderating effect of category-specific online buying experience,
it also has important limitations and points to several interesting
areas for additional research. For one, more refined definitions of
the category allocation factors could help to obtain a better insight
into their effect on online buying behavior. For instance, a focus
on assortment composition in addition to size may lead to
additional and more refined insights. Likewise, using a household-
specific rating of impulsiveness (rather than assuming it is a
characteristic that is constant across consumers) or allowing price
and assortment to vary over time are important refinements that
are worthwhile to investigate in more depth. Second, it would be
valuable to obtain an in-depth insight into experience effects and
how they work, exploring their impact on mediating variables
such as learning processes and online retailer trust. Third,
an interesting extension of our study would be to explore
cross-category effects, such as the potential weakening effect of
buying one sensory category as experience reduction for another
sensory category, or the accumulated negative effect of encoun-
tering a large number of categories with price and assortment
disadvantages. Fourth, because of data availability, the focus of
this paper is on consumers’ shopping behavior in a single chain

multichannel grocery context. While this approach has the
advantage of eliminating confounding effects of differences in
assortment composition and retail strategy across different grocery
chains, for instance and although previous research has demon-
strated that the large majority of multichannel shoppers visit the
same chain in the online and offline channel (Melis et al. 2013), a
more detailed and complete analysis could be carried out if data of
competitive chains would also be available. This would allow for a
simultaneous analysis of category allocation decisions over
different channels and chains providing a more complete picture
of the complex competitive relationships in a multichain
multichannel retail context. Finally, examining the impact of
category allocation decisions in a non-grocery shopping context
(where characteristics like perishability overlap less with sensory
characteristics) could offer a useful and interesting extension.

Acknowledgments

The authors thank the online grocery retailer who provided
the data used in this study, and Huiying He and Kim Goeleven
for their help. They further thank Koert van Ittersum, Siegfried
Dewitte, Vera Blazevic and Lien Lamey for their helpful
suggestions on previous versions of this article.

Appendix A. Supplementary Data

Supplementary data to this article can be found online at
http://dx.doi.org/10.1016/j.intmar.2015.04.001.

References

Alba, Joseph and J. Wesley Hutchinson (1987), “Dimensions of Consumer
Expertise,” Journal of Consumer Research, 13, 4, 411–54.

———, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan
Sawyer, and Stacy Wood (1997), “Interactive Home Shopping: Consumer,
Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces,”
Journal of Marketing, 61, 3, 38–53.

Andrews, Rick L., Andrew Ainslie, and Imran S. Currim (2002), “An Empirical
Comparison of Logit Choice Models With Discrete Versus Continuous
Representations of Heterogeneity,” Journal of Marketing Research, 39, 4,
479–87.

Ansari, Asim, Carl F. Mela, and Scott A. Neslin (2008), “Customer Channel
Migration,” Journal of Marketing Research, 45, 1, 60–76.

Avery, Jill, Thomas J. Steenburgh, John Deighton, and Mary Caravella (2012),
“Adding Bricks to Clicks: Predicting the Patterns of Cross-channel
Elasticities Over Time,” Journal of Marketing, 76, 3, 96–111.

Babin, Barry J. and William R. Darden (1995), “Consumer Self-regulation in a
Retail Environment,” Journal of Retailing, 71, 1, 47–70.

Baltas, George, Paraskevas C. Argouslidis, and Dionysis Skarmeas (2010),
“The Role of Customer Factors in Multiple Store Patronage: A Cost–
Benefit Approach,” Journal of Retailing, 86, 1, 37–50.

Bass, Frank M., Norris Bruce, Sumit Majumdar, and B.P.S. Murthi (2009),
“Wearout Effects of Different Advertising Themes: A Dynamic Bayesian
Model of the Advertising-sales Relationship,” Marketing Science, 26, 2,
179–95.

Bell, David R., Jeongwen Chian, and V. Padmanabhan (1999), “The
Decomposition of Promotional Response: An Empirical Generalization,”
Marketing Science, 18, 4, 504–26.

Bellman, Steven, Jamie Murphy, Shiree Treleaven-Hassard, James O’Farrell, Lili
Qiu, and Duane Varan (2013), “Using Internet Behavior to Deliver Relevant
Television Commercials,” Journal of Interactive Marketing, 27, 2, 130–40.

http://dx.doi.org/10.1016/j.intmar.2015.04.001

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0010

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0005

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0015

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0020

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0025

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0030

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0035

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0040

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0045

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0050

77K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

Borle, Sharad, Peter Boatwright, Joseph B. Kadane, Joseph C. Nunes, and Galit
Shmueli (2005), “The Effect of Product Assortment Changes on Customer
Retention,” Marketing Science, 24, 4, 616–22.

Breugelmans, Els and Katia Campo (2011), “Effectiveness of In-store
Displays in a Virtual Store Environment,” Journal of Retailing, 87, 1,
75–89.

Cheng, Jacqui (2010), “The New Age of Online Grocery Shopping,” [Available
at http://arstechnica.com/web/news/2010/03/the-new-age-of-online-grocery-
shopping.ars].

Chintagunta, Pradeep K., Junhong Chu, and Javier Cebollada (2012), “Quantifying
Transaction Costs in Online/Offline Grocery Channel Choice,” Marketing
Science, 31, 1, 96–114.

Chu, Junhong, Pradeep Chintagunta, and Javier Cebollada (2008), “A
Comparison of Within-household Price Sensitivity Across Online and
Offline Channels,” Marketing Science, 27, 2, 283–99.

———, Marta Arce-Urriza, José-Javier Cebollada-Calvo, and Pradeep K.
Chintagunta (2010), “An Empirical Analysis of Shopping Behavior Across
Online and Offline Channels for Grocery Products: The Moderating Effects of
Household and Product Characteristics,” Journal of Interactive Marketing, 24,
4, 251–68.

Cleeren, Kathleen, Harald van Heerde, and Marnik Dekimpe (2013), “Rising
From the Ashes: How Brand and Categories Can Overcome Product-harm
Crises,” Journal of Marketing, 77, 2, 58–77.

Cooper, Lee and Masao Nakanishi (1996), Market Share Analysis, International
Series in Quantitative Marketing. Kluwer Academic Publishers.

Degeratu, Alexandru M., Arvind Rangaswamy, and Jianan Wu (2000),
“Consumer Choice Behavior in Online and Traditional Supermarkets: The
Effects of Brand Name, Price, and Other Search Attributes,” International
Journal of Research in Marketing, 17, 1, 55–78.

Dhar, Ravi (1997), “Consumer Preference for a No-choice Option,” Journal of
Consumer Research, 24, 2, 215–31.

Dholakia, Utpal M., Barbara E. Kahn, Randy Reeves, Aric Rindfleisch, David
Stewart, and Earl Taylor (2010), “Consumer Behavior in a Multichannel,
Multimedia Retailing Environment,” Journal of Interactive Marketing, 24,
2, 86–95.

Foekens, Eijte W., Peter S.H. Leeflang, and Dick R. Wittink (1999), “Varying
Parameter Models to Accommodate Dynamic Promotion Effects,” Journal
of Econometrics, 89, 1/2, 249–68.

Fox, Edward J. and Stephen Hoch (2005), “Cherry Picking,” Journal of
Marketing, 69, 1, 46–62.

Frambach, Ruud T., Henk C.A. Roest, and Trichy V. Krishnan (2007), “The
Impact of Consumer Internet Experience on Channel Preference and Usage
Intentions across the Different Stages of the Buying Process,” Journal of
Interactive Marketing, 21, 2, 26–41.

Gijsbrechts, Els, Katia Campo, and Patricia Nisol (2008), “Beyond Promotion-
based Store Switching: Antecedents and Patterns of Systematic Multiple-
store Shopping,” International Journal of Research in Marketing, 25, 1,
5–21.

Gupta, Sumeet and Hee-Woong Kim (2010), “Value-driven Internet Shopping:
The Mental Accounting Theory Perspective,” Psychology & Marketing, 27,
1, 13–35.

Hamilton, Rebecca W. and Debora Viana Thompson (2007), “Is There a
Substitute for Direct Experience? Comparing Consumers’ Preferences After
Direct and Indirect Product Experiences,” Journal of Consumer Research,
34, 4, 546–55.

Hoch, Stephen J. (2002), “Product Experience Is Seductive,” Journal of Consumer
Research, 29, 3, 448–54.

Hoyer, Wayne D. and Deborah J. MacInnis (2010), Consumer Behavior. Mason:
South-Western Cengage Learning.

Huffman, Cynthia and Barbara E. Kahn (1998), “Variety for Sale: Mass
Customization or Mass Confusion?” Journal of Retailing, 74, 4, 491–513.

Iyengar, Raghuram, Asim Ansari, and Sunil Gupta (2007), “A Model of
Consumer Learning for Service Quality and Usage,” Journal of Marketing
Research, 44, 4, 529–44.

Jiménez, Fernando and Norma A. Mendoza (2013), “Too Popular to
Ignore: The Influence of Online Reviews on Purchase Intentions of
Search and Experience Products,” Journal of Interactive Marketing,
27, 3, 226–35.

Kabadayi, Sertan, Nermin Eyuboglu, and Gloria P. Thomas (2007), “The
Performance Implications of Designing Multiple Channels to Fit with
Strategy and Environment,” Journal of Marketing, 71, 4, 195–211.

Kamakura, Wagner A. and Gary J. Russell (1989), “A Probabilistic Choice
Model for Market Segmentation and Elasticity Structure,” Journal of
Marketing Research, 26, 4, 379–90.

Kim, Dan J., Donald L. Ferrin, and H. Raghav Rao (2008), “A Trust-based
Consumer Decision-making Model in Electronic Commerce: The Role of
Trust, Perceived Risk, and their Antecedents,” Decision Support Systems,
44, 2, 544–64.

Konuş, Umut, Peter C. Verhoef, and Scott A. Neslin (2008), “Multichannel
Shopper Segments and their Covariates,” Journal of Retailing, 84, 4,
398–413.

Kopalle, Praveen K., Carl F. Mela, and Lawrence Marsh (1999), “The Dynamic
Effect of Discounting on Sales: Empirical Analysis and Normative Pricing
Implications,” Marketing Science, 18, 3, 317–32.

Laroche, Michel, Zhiyong Yang, Gordon H.G. McDougall, and Jasmin
Bergeron (2005), “Internet versus Bricks-and-Mortar Retailers: An
Investigation into Intangibility and Its Consequences,” Journal of Retailing,
81, 4, 251–67.

Leenheer, Jorna, Harald van Heerde, Tammo Bijmolt, and Ale Smidts (2007),
“Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical
Analysis Accounting for Self-selecting Members,” International Journal of
Research in Marketing, 24, 1, 31–47.

Lesaffre, Emmanuel, Dimitris Rizopulos, and Roula Tsonaka (2007), “The
Logistic Transform for Bounded Outcome Scores,” Biostatistics, 8, 1, 72–85.

Luan, Jackie and K. Sudhir (2010), “Forecasting Marketing Mix Responsive-
ness for New Products,” Journal of Marketing Research, 47, 3, 444–57.

McPartlin, Sue and Lisa Dugal (2012), “Understanding How US Online
Shoppers Are Reshaping the Retail Experience,” [Available at http://www.
pwc.com/en_us/us/retail-consumer/publications/assets/pwc-us-multichannel-
shopping-survey ].

Melis, Kristina, Katia Campo, Els Breugelmans, and Lien Lamey (2013),
“Buying Groceries Online: Which Factors Drive Online Store Choice?” .

Neslin, Scott A., Dhruv Grewal, Robert Leghorn, Venkatesh Shankar, Marije L.
Teerling, Jacquelyn S. Thomas, and Peter C. Verhoef (2006), “Challenges
and Opportunities in Multichannel Customer Management,” Journal of
Service Research, 9, 2, 95–112.

——— and Venkatesh Shankar (2009), “Key Issues in Multichannel Customer
Management: Current Knowledge and Future Directions,” Journal of
Interactive Marketing, 23, 1, 70–81.

Nielsen (2014), “Convenience, Continuous Innovation Key to Retail
Success,” [Available at http://www.progressivegrocer.com/nielsen-convenience-
continuous-innovation-key-retail-success#sthash.huriH3eL.dpuf].

Oppewal, Harmen and Kitty Koelemeijer (2005), “More Choice Is Better:
Effects of Assortment Size and Composition on Assortment Evaluation,”
International Journal of Research in Marketing, 22, 1, 45–60.

Pauwels, Koen, Peter S.H. Leeflang, Marije L. Teerling, and K.R. Eelko
Huizingh (2011), “Does Online Information Drive Offline Revenues? Only
for Specific Products and Consumer Segments!,” Journal of Retailing, 87,
1, 1–17.

Peck, Joann and Terry L. Childers (2003), “To Have and to Hold: The Influence
of Haptic Information on Product Judgments,” Journal of Marketing, 67, 2,
35–48.

Petrin, Amil and Kenneth Train (2010), “A Control Function Approach to
Endogeneity in Consumer Choice Models,” Journal of Marketing Research,
47, 1, 3–13.

Pozzi, Andrea (2012), “Shopping Cost and Brand Exploration in Online
Grocery,” American Economic Journal: Microeconomics, 43, 3, 96–120.

Punj, Girish (2011), “Effect of Consumer Beliefs on Online Purchase Behavior:
The Influence of Demographic Characteristics and Consumption Values,”
Journal of Interactive Marketing, 25, 3, 134–44.

Purnawirawan, Nathalia, Patrick De Pelsmacker, and Nathalie Dens (2012),
“Balance and Sequence in Online Reviews: How Perceived Usefulness Affects
Attitudes and Intentions,” Journal of Interactive Marketing, 26, 4, 244–55.

Shankar, Venkatesh and Manjit S. Yadav (2010), “Emerging Perspectives on
Marketing in a Multichannel and Multimedia Retailing Environment,”
Journal of Interactive Marketing, 24, 2, 55–7.

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0055

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0060

http://arstechnica.com/web/news/2010/03/the-new-age-of-online-grocery-shopping.ars

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0065

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0075

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0070

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0080

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0085

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0090

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0095

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0105

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0320

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0120

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0110

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0125

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0130

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0135

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0140

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0145

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0150

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0155

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0160

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0165

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0170

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0175

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0180

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0185

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0325

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0195

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0200

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0205

http://www.pwc.com/en_us/us/retail-consumer/publications/assets/pwc-us-multichannel-shopping-survey

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0335

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0215

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0220

http://www.progressivegrocer.com/nielsen-convenience-continuous-innovation-key-retail-success#sthash.huriH3eL.dpuf

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0225

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0345

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0235

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0240

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0245

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0250

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0255

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0265

78 K. Campo, E. Breugelmans / Journal of Interactive Marketing 31 (2015) 63–78

Sloot, Laurens M., Dennis Fok, and Peter C. Verhoef (2006), “The Short- and
Long-term Impact of an Assortment Reduction on Category Sales,” Journal
of Marketing Research, 43, 4, 536–48.

Urban, Glen L., Cinda Amyx, and Antonio Lorenzon (2009), “Online Trust:
State of the Art, New Frontiers, and Research Potential,” Journal of
Interactive Marketing, 23, 2, 179–90.

Venkatesan, Rajkumar, V. Kumar, and Nalini Ravishanker (2007), “Multichannel
Shopping: Causes and Consequences,” Journal of Marketing, 71, 2, 114–32.

Vermunt, Jeroen K. and Jay Magidson (2013), Technical Guide for Latent GOLD
5.0: Basic, Advanced and Syntax. Belmont Massachusetts: Statistical Innovations
Inc.

Vroegrijk, Mark, Els Gijsbrechts, and Katia Campo (2013), “Close Encounter
With the Hard Discounter: A Multiple-store Shopping Perspective on the
Impact of Local Hard-discounter Entry,” Journal of Marketing Research,
50, 5, 606–26.

Warschun, Mirko (2012), “A Fresh Look at Online Grocery,” [Available at http://
www.atkearney.com/paper/-/asset_publisher/dVxv4Hz2h8bS/content/a-fresh-
look-at-online-grocery/10192].

Weathers, Danny, Subhash Sharma, and Stacy L. Wood (2007), “Effects of
Online Communication Practices on Consumer Perceptions of Performance
Uncertainty for Search and Experience Goods,” Journal of Retailing, 83, 4,
393–401.

Wolk, Agnieszka and Christine Ebling (2010), “Multi-channel Price Differen-
tiation: An Empirical Investigation of Existence and Causes,” International
Journal of Research in Marketing, 27, 2, 142–50.

Wooldridge, Jeffrey M. (2013), Econometric Analysis of Cross Section and
Panel Data. second edition. Cambridge, MA: MIT Press.

Zhang, Jie, Paul W. Farris, John W. Irvin, Tarun Kushwaha, and Thomas J.
Steenburgh (2010), “Crafting Integrated Multichannel Retailing Strategies,”
Journal of Interactive Marketing, 24, 2, 168–80.

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0270

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0275

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0285

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0350

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0290

http://www.atkearney.com/paper/-/asset_publisher/dVxv4Hz2h8bS/content/a-fresh-look-at-online-grocery/10192

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0295

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0300

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0305

http://refhub.elsevier.com/S1094-9968(15)00029-8/rf0310

Buying Groceries in Brick and Click Stores: Category Allocation Decisions and the Moderating Effect of Online Buying Experience

Introduction
Conceptual Framework
Acquisition Utility: The Impact of Marketing Mix Instruments
Assortment Differences
Price Differences
Promotion Differences
In-store Stimuli
Transaction Utility: The Impact of Perceived Purchase Risk and Shopping Convenience
Perceived Purchase Risk
Shopping Convenience
Moderating Impact of Online Buying Experience
Model
Data
Empirical Results
Discussion and Conclusions
Factors of Multichannel Purchase Allocation Decisions
The Moderating Effect of Category-specific Online Buying Experience
Managerial Implications
Directions for Further Research
Acknowledgments
Appendix A. Supplementary Data
References

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