Exploring decisive factors affecting an organization’s SaaS adoption: A case study

Wu, Wei-Wen, Lan, Lawrence W. & Lee, Yu-Ting (2011): Exploring decisive factors affecting an organization’s SaaS adoption: A case study, International Journal of Information Management, Vol. 31, Issue 6, p.556-563. 6p. DOI: 10.1016/j.ijinfomgt.2011.02.007

Keywords: Software as a Service (SaaS), Adoption, Trust, Decision Making Trial and Evaluation, Laboratory (DEMATEL)

Wu, Wei-Wen, Lan, Lawrence W. & Lee, Yu-Ting start by defining cloud services as a group of service solutions involving computing, data storage, and software available through the Internet where customers do not own or operate the service provided. The cloud process all the information given by the user to then send back its results. This model allows organizations to focus on its core business and lessens the burden of developing and maintaining complicated IT systems.

Cloud computing can be divided into three categories: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Nevertheless, SaaS is regarded as potentially the most important model for scaling IT performance. Despite this information, organizations are still hesitant to use it due to trust concerns. It is stated that SaaS has an attractive growth potential as SMEs have yet to start using SaaS extensively. Among the reasons why it is not yet widely-spread in this segment, concern about data security represents the stronger factor. Therefore, focusing on trust to highlight perceived benefits and diminish perceived risks is the approach recommended for all marketing efforts. Nonetheless, adopting new technologies or services solutions is still commonly seen as a way to improve competitiveness in an organization.

Trust plays a decisive role in the acceptance of perceived benefits and the lessening of perceived risks for this business model. This is why, the authors propose a solution framework where they treat perceived benefits and perceived risks as two different arguments in order to develop a visible cause-effect graph to aid organizations in their decision making towards SaaS. They define trust as the willingness to behave risky in uncertain situations as it is believed that expected benefits might overcome the negative aspects.

Although trust is a main driver for the accomplishment of any e-commerce, perceived risks, technical and subjective, act as barriers for the adoption of this service, while perceived benefits represent a motivation for adoption. The study aims to select the most important set of perceived benefits and perceived risks for SaaS adoption using the DEMATEL model to conduct a cause-effect analysis. To do so, they use a case study centering on a Taiwanese technological company. Out of all the factors studied, it is concluded that an easy and fast deployment of the service, and its potential in the future are the most relevant benefits to business. On the other hand, data locality and security, and authentication and authorization are mentioned as the most important perceived risks preventing adoption. These perceived risks are, however, subjective rather than technical, as organizations commonly dislike the lack of ownership and control on cloud computing deployment.

These results tell us that SaaS businesses should emphasize the subjective but strategical aspects of delegating security control to SaaS. The writers divide two types of SaaS customers: the organizations focusing on perceived benefits, where SaaS vendors can focus on their strategical competitive advantage, and the organizations focusing on perceived risks, where vendors should reduce security concerns by communicating best practices of successful businesses using SaaS, and expert recommendations.



Catteddu, D., & Hogben, G. (2009). Cloud computing: Benefits, risks and recom- mendations for information security. European Network and Information Security Agency (ENISA), 1–125.

Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850–1858.

Chen, J. K., & Chen, I. S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981–1990.

Cho, S. E. (2010). Perceived risks and customer needs of geographical accessibil- ity in electronic commerce. Electronic Commerce Research and Applications, 9(6), 495–506.

Crespo, A. H., & del Bosque, I. R. (2010). The influence of the commercial features of the Internet on the adoption of e-commerce by consumers. Electronic Commerce Research and Applications, 9(6), 562–575.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end- user information systems: Theory and results. Doctoral dissertation. Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Deutsch, M. (1962). Cooperation and trust: Some theoretical notes. In M. R. Jones (Ed.), Nebrasca symposium on motivation (pp. 275–318). Nebrasca: University Press.

Elahi, S., & Hassanzadeh, A. (2009). A framework for evaluating electronic commerce adoption in Iranian companies. International Journal of Information Management, 29(1), 27–36.

Evans, A. M., & Krueger, J. I. (2010). Elements of trust: Risk and perspective-taking. Journal of Experimental Social Psychology, doi:10.1016/j.jesp.2010.08.007

Feuerlicht, G. (2010). Next generation SOA: Can SOA survive cloud computing? In V. Snasel, V. Snasel, et al. (Eds.), Advances in intelligent web mastering – 2, AISC 67 (pp. 19–29).

Fontela, E., & Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 report. Geneva, Switzerland: BATTELLE Institute, Geneva Research Center.

Gabus, A., & Fontela, E. (1973). Perceptions of the World Problematique: Communi- cation Procedure. In Communicating with those bearing collective responsibility (DEMATEL Report No. 1). Geneva, Switzerland: BATTELLE Institute, Geneva Research Centre.

Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Geneva, Switzerland: BATTELLE Institute, Geneva Research Centre.

Goscinski, A., & Brock, M. (2010). Toward dynamic and attribute based publica- tion, discovery and selection for cloud computing. Future Generation Computer Systems, doi:10.1016/j.future.2010.03.009

Ho, W. R. J., Tsai, C. L., Tzeng, G. H., & Fang, S. K. (2011). Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM. Expert Systems with Applications, 38(1), 16–25.

Hu, H. Y., Lee, Y. C., Yen, T. M., & Tsai, C. H. (2009). Using BPNN and DEMATEL to modify importance–performance analysis model—A study of the computer industry. Expert Systems with Applications, 36(6), 9969–9979.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.

Lin, Ch. J., & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205–213. Lopez-Nicolas, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of

advanced mobile services acceptance: Contributions from TAM and diffusion

theory models. Information & Management, 45(6), 359–364.

Mehrtens, J., Cragg, P. B., & Mills, A. M. (2001). A model of Internet adoption by SMEs.

Information & Management, 39(3), 165–176.

Misra, S. C., & Mondal, A. (2010). Identification of a company’s suitability for the

adoption of cloud computing and modelling its corresponding return on invest-

ment. Mathematical and Computer Modelling, doi:10.1016/j.mcm.2010.03.037 Shen, Y. C., Lin, G. T. R., & Tzeng, G. H. (2011). Combined DEMATEL techniques with novel MCDM for the organic light emitting diode technology selection. Expert

Systems with Applications, 38(3), 1468–1481.

Shieh, J. I., Wu, H. H., & Huang, K. K. (2010). A DEMATEL method in identifying

key success factors of hospital service quality. Knowledge-Based Systems, 23(3), 277–282.

Slovic, P. (1992). Perception of risk: Reflections on the psychometric paradigm. In S. Krimsky, & D. Golding (Eds.), Social theories of risk (pp. 117–152). New York: Praeger.

Subashini, S., & Kavitha, V. (2010). A survey on security issues in service deliv- ery models of cloud computing. Journal of Network and Computer Applications, doi:10.1016/j.jnca.2010.07.006

Sultan, N. (2010). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116.

Tarafdar, M., & Vaidya, S. D. (2006). Challenges in the adoption of E-Commerce technologies in India: The role of organizational factors. International Journal of Information Management, 26(6), 428–441.

Tsai, W. H., & Chou, W. C. (2009). Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert Systems with Applications, 36(2), 1444–1458.

Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Systems with Applica- tions, 36(4), 7738–7748.

Tzeng, G. H., Chiang, C. H., & Li, C. W. (2007). Evaluating intertwined effects in e- learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028–1044.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Wu, H. H., Chen, H. K., & Shieh, J. I. (2010). Evaluating performance criteria of Employ- ment Service Outreach Program personnel by DEMATEL method. Expert Systems

with Applications, 37(7), 5219–5223.

Wu, F., Li, H. H., & Kuo, Y. H. (2010). Reputation evaluation for choosing a trustworthy

counterparty in C2C e-commerce. Electronic Commerce Research and Applications,


Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies

using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.

Wu, W. W. (2008). Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35(3), 828–835. Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in

emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252.

Integrating marketing communications: new findings, new lessons and new ideas

Rajeev Batra and Kevin Lane Keller (2016): Integrating Marketing Communications: New Findings, New Lessons, and New Ideas. Journal of Marketing, Vol. 80 Issue 6, p122-145. 24p. DOI: http://dx.doi.org/10.1509/jm.15.0419

Keywords: marketing communications, marketing integration, integrated marketing communications, traditional media, digital media

New media has impacted consumers in a drastic way, changing media usage patterns and disturbing how information is sought, where consumers look for it, and how the decide to choose a brand. Nowadays, increase of popularity in multitasking has led customers to a continuous state of partial attention. Consumers have a different dynamic when taking a decision to purchase due to search engines, blogs, brand websites, etc. These new tools lead consumers to actively seek information rather than passively receive it from traditional media.

The current situation has lead word-of-mouth and brand advocacy to be vital to current communication strategies, however, this has reduced marketer’s control over the information that arrives to customers. Nevertheless, these new trends improve personalization, content, location and timing of the communications and opens new possibilities for accomplishing their objectives as marketers have a wider selection of communication possibilities.

Due to the numerous communication channels, marketers must think about the message as well as the context of their communication or “interactive effects”. It is mentioned that there is interaction between new and old media such as TV, social media, mobiles, off-line word-of-mouth, etc.

To adapt to the new situation, the author proposes two communication models:

  1. “Bottoms-up” communication matching model: identifies communication options with the best ability to satisfy a customer at different stages of the consumer decision journey.
  2. “Top-down” communication optimization model: evaluates the design of a marketing communication program with relevant criteria to how it is integrated to short-term sales goals and long-term brand equity.

As for integrating marketing communications, two types of approaches are discussed. First, the micro approaches using consumer psychology and information processing principles to explore the impact of multi-media campaigns in different communication goals. Then, a second approach using econometric techniques to assess the effect of multi-media campaigns at brand-level. Additionally, consistency, complementarity and cross-effect among media and communication options are mentioned as the three most important factor for a successful integrated marketing communications program.

A new consumer decision journey circle is mentioned. The new concept begins by the consideration of an initial set of brands which the potential user forms a first consideration, then selects a brand based on this knowledge to finally, use the product or service and create post-purchase experiences that will shape future interactions with the brand.

Later, an analysis of each media and its impact on the effectiveness of the communication is discussed:

  • Traditional media: It is still relevant even today. It is mentioned that the message communicates is more important than repetition. Nevertheless, advertising effects vary on the channel used.
  • Newer online media includes:
    • Search ads: users who search for specific and less popular keywords are said to be closer to a purchase decision. Allowing paid search ads to potentially increase click-through rate and conversion rates
    • Display ads: This type of advertisement can increase visitation to business-websites for most users in the purchase funnel. Nevertheless, this has considerable less impact on potential customers who already visited the website but failed to engage.
    • Websites: This channel can be more effective when it matches its potential customer’s intellectual style. Additionally, age, gender and geographical location segmentation can also affect success.
    • E-mail: Increases purchases three more times than social media and personalization of said emails is shown to improve its effectiveness.
    • o Social Media: Brand-generated content can positively affect valence, receptivity and customer susceptibility. Nevertheless, social media must not focus on a single platform as this can lead to misleading brand sentiments.
    • Mobile: Users in this media tend to go directly to a brand’s website or app. Users also make more purchases driven by impulse than product features. Additionally, coupons and ads have shown to be the most effective when personalized to the user’s taste, location and time of the day.


Aaker, Jennifer (1997), “Dimensions of Brand Personality,” Journal of Marketing Research, 34 (August), 347–357.

Abernathy, Avery M. (1992), “The Information Content of Newspaper Advertising,” Journal of Current Issues and Research in Advertising, (14, 2), 63-68.

Alba, Joseph W. and J. Wesley Hutchinson (1987), “Dimensions of Consumer Expertise,” Journal of Consumer Research, 13 (March), 411-454.

Allenby, Greg and Dominique Hanssens, “Advertising Response,” Marketing Science Institute, Special Report, No. 05-200, 2005.

Amaldoss, Wilfred and Chuan He (2010), “Product Variety, Informative Advertising, and Price Competition,” Journal of Marketing Research 47 (February), pp. 146–56.

Anderl, Eva, Ingo Becker, Florian Wangenheim, Jan Schumann (2014), “Mapping the Customer Journey: A Graph-Based Framework for Online Attribution Modeling,” DOI: 10.2139/ssrn.2343077

Andrews, Michelle, Xueming Luo, Zheng Fang, and Anindya Ghose (in press), “Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness” Marketing Science, forthcoming.

Assael, Henry (2011), “From Silos to Synergy: A Fifty-Year Review of Cross-Media Research Shows Synergy Has Yet to Achieve its Full Potential,” Journal of Advertising Research, 51 (1), pp. 42-48.

Assmus, Gert, John U. Farley, and Donald R. Lehmann (1984), “How Advertising Affects Sales: Meta-analysis of Econometric Results,” Journal of Marketing Research, 21 (February), pp. 65-74.

Ataman, M. Berk, Harald J. Van Heerde, Carl F. Mela (2010), “The Long-Term Effect of Marketing Strategy on Brand Sales,” Journal of Marketing Research, 47 (October), pp. 866- 882.

Aufreiter, Nora, Julien Boudet and Vivian Weng (2014), “Why Marketers Keep Sending You E- Mails,” McKinsey Quarterly, January.

Bagozzi, Richard P. and Utpal Dholakia (1999), “Goal Setting and Goal Striving in Consumer Behavior,” Journal of Marketing, 63 (Special Issue), pp. 19-32.

Barroso, Alicia and Gerard Llobet (2012), “Advertising and Consumer Awareness of New, Differentiated Products,” Journal of Marketing Research, 49 (December), pp. 773-792.

Batra, Rajeev, Aaron Ahuvia, and Richard P. Bagozzi (2012), “Brand Love,” Journal of Marketing, 76 (March), pp. 1-16.

Batra, Rajeev and Olli T. Ahtola (1990), “Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes,” Marketing Letters, 2 (2), 159-170.

Batra, Rajeev and Pamela M. Homer (2004), “The Situational Impact of Brand Image Beliefs,” Journal of Consumer Psychology, 14 (3), pp. 318-330.


Batra, Rajeev and Michael L. Ray (1986), “Situational Effects of Advertising Repetition: The Moderating Influence of Motivation, Ability, and Opportunity to Respond,” Journal of Consumer Research, 12 (March), 432-445.

Belch, George E. and Michael A. Belch (2015), Advertising and Promotions: An Integrated Marketing Communications Perspective, 10th ed. (New York: McGraw-Hill).

Belk, Russell W. (1988), “Possessions and the Extended Self,” Journal of Consumer Research, 15 (September), pp. 139-168.

Bell, David (2014), Location Is (Still) Everything: The Surprising Influence of the Real World on How … World on How We Search, Shop, and Sell in the Virtual One, New Harvest, 2014.

Brakus, J. Joško, Bernd H. Schmitt, Lia Zarantonello (2009), “Brand Experience: What Is It? How Is It Measured? Does It Affect Loyalty?,” Journal of Marketing, 73 (May), pp. 52-68.

Bell, David (2014), Location Is (Still) Everything: The Surprising Influence of the Real World on How We Search, Shop, and Sell in the Virtual One, New Harvest.

Berger, Jonah (2014), “Word-of-Mouth and Interpersonal Communication: A Review and Directions for Future Research,” Journal of Consumer Psychology, 24 (4), pp. 586-607.

Berman, Ron and Zsolt Katona (2013), “The Role of Search Engine Optimization in Search Marketing,” Marketing Science, 32 (July-August), pp. 644-651.

Berry, Leonard L. (2000), “Cultivating Service Brand Equity,” Journal of the Academy of Marketing Science, 28 (1), pp. 128-137.

Bloemer, José M. M. and Hans D. P. Kasper (1995), “The Complex Relationship Between Consumer Satisfaction and Brand Loyalty,” Journal of Economic Psychology, 16 (July), pp. 311-329.

Bonfrer, André and Xavier Drèze (2009), “Real-Time Evaluation of Email Campaign Performance,” Marketing Science, 28 (2), 251-63.

Braun, Michael and Wendy Moe (2013), “Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories,” Marketing Science, 32 (September/October), pp. 753-767.

Briggs, Rex, R. Krishnan and Norm Borin (2005), “Integrated Multichannel Communication Strategies: Evaluating the Return on Marketing Objectives—the Case of the 2004 Ford F-150 Launch,” Journal of Interactive Marketing, 19 (3), pp. 81-90.

Brown, Jacqueline Johnson, and Peter H. Reingen (1987), “Social Ties and Word-of-Mouth Referral Behavior,” Journal of Consumer Research, 14 (December), pp. 350-362.

Brown, Stephen, Robert V. Kozinets, and John F. Sherry Jr. (2003), “Teaching Old Brands New Tricks: Retro Branding and the Revival of Brand Meaning,” Journal of Marketing, 67 (3), pp. 19–33.

Bruce, Norris I., Natasha Zhang Foutz, and Ceren Kolsarici (2012), “Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products,” Journal of Marketing Research, 49 (August), pp. 469-486.

Campbell, Margaret C. and Amna Kirmani (2008), “I Know What You’re Doing and Why You’re Doing it: The Use of the Persuasion Knowledge Model in Consumer Research,” in


Curt Haugtvedt, Paul Herr, and Frank Kardes, eds., Handbook of Consumer Psychology, Psychology Press: New York, pp. 549-574.

Chang, Yuhmiin and Esther Thorson (2004), “Television and Web Advertising Synergies,” Journal of Advertising, 33 (2), 75-84.

Chaudhuri, Arjun, and Morris B. Holbrook (2001), “The Chain of Effects From Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty,” Journal of Marketing, 65, (April), pp. 81-93.

Chevalier, Judith A. and Dina Mayzlin (2006), “The Effect of Word of Mouth on Sales: Online Book Reviews,” Journal of Marketing Research, 43 (August), pp. 345-354.

Court, David, David Elzinga, Susan Mulder, and Ole Jorgen Vetvik (2009), “The Consumer Decision Journey,” McKinsey Quarterly, June.

Cox, Daniel F. (1967), Risk-taking and Information-Handling in Consumer Behavior (Boston: Harvard University Press).

Danaher, Peter J., Janghyuk Lee, and Laoucine Kerbache (2010), “Optimal Internet Media Selection,” Marketing Science 29 (March–April), pp. 336–47.

Danaher, Peter J., Guy W. Mullarkey, and Skander Essegaier (2006), “Factors Affecting Web Site Visit Duration: A Cross-Domain Analysis,” Journal of Marketing Research, 43 (May), pp. 182–94.

Danaher, Peter J., André Bonfrer, and Sanjay Dhar (2008), “The Effect of Competitive Advertising,” Journal of Marketing Research, 45 (April), pp. 211–25.

Danaher, Peter J. and Tracey S. Dagger (2013), “Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign,” Journal of Marketing Research, 50 (August), pp. 517-534.

de Vries, Lisette, Sonja Gensler, and Peter S. H. Leeflang (2012), “Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing,” Journal of Interactive Marketing, 26 (2), 83-91.

Dijkstra, Majorie, Heidi E. J. J. M. Buijtels and W. Fred van Raaij (2005), “Separate and Joint Effects of Medium Type on Consumer Responses: A Comparison of Television, Print, and the Internet,” Journal of Business Research, 58 (March), pp. 377-386.

Dinner, Isaac M., Harald J. Van Heerde, and Scott A. Neslin (2014), “Driving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of Marketing Research, 51 (October ), pp. 527-545.

Draganska, Michaela, Wesley R. Hartmann, and Gena Stanglein (2014), “Internet Versus Television Advertising: A Brand-Building Comparison,” Journal of Marketing Research, 51 (October), pp. 578-590.

Duncan, Tom and Frank Mulhern (2004) “A White Paper on the Status, Scope, and Future of IMC,” eds., March 2004, Daniels College of Business at the University of Denver.

Edell, Julie A., and Marian Chapman Burke (1987), “The Power of Feelings in Understanding Advertising Effects,” Journal of Consumer Research, 14 (December), pp. 421-433.


53 Edell, Julie A. and Kevin Lane Keller (1989), “The Information Processing of Coordinated

Media Campaigns,” Journal of Marketing Research, 26 (May), pp. 149–163.

Edell, Julie A. and Kevin Lane Keller (1999), “Analyzing Media Interactions: The Effects of Coordinated Print-TV Advertising Campaigns,” Marketing Science Institute Report No. 99– 120.

Ericson, Liz, Louise Herring, and Kelly Ungerman (2014), “Busting Mobile Shopping Myths,” McKinsey Quarterly, December.

Fang, Zheng, Xueming Luo and Megan E. Keith (2014), “How Effective is Location-Targeted Mobile Advertising,” MIT Sloan Management Review, October 2014, pp. 14-15.

Fournier, Susan (1997), “Consumers and Their Brands: Developing Relationship Theory in Consumer Research,” Journal of Consumer Research 24 (3), 343–373.

Freimer, Marshall and Dan Horsky (2012), “Periodic Advertising Pulsing in a Competitive Market,” Marketing Science, 31 (July-August), pp. 637-648

Friestad, Marian, and Peter Wright (1994), “The Persuasion Knowledge Model: How People Cope with Persuasion Attempts,” Journal of Consumer Research, 21 (June), pp. 1-31.

Gatignon, Hubert and Dominique M. Hanssens, “Modeling Marketing Interactions with Application to Sales Force Effectiveness,” Journal of Marketing Research, 24 (August), pp. 247-257.

Ghose, Anindya (2015), “Cynicism About Mobile Advertising is Greatly Misplaced,” The Conversation, August 19.

Godes, David, and Dina Mayzlin (2004), “Using Online Conversations to Study Word-of-Mouth Communication,” Marketing Science, 23 (November), pp. 545-560.

Goldenberg, Jacob, Gal Oestreicher-Singer, and Shachar Reichman, “The Quest for Content: How User-Generated Links Can Facilitate Online Exploration,” Journal of Marketing Research, 49 (August 2012), pp. 452-468.

Gopalakrishna, Srinath and Rubikar Chatterjee (1992), “A Communications Response Model for a Mature Industrial Product: Applications and Implications,” Journal of Marketing Research, 29 (May), pp. 189-200.

Gopinath, Shyam, Jacquelyn S. Thomas, and Lakshman Krishnamurthi (2014), “Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance,” Marketing Science, 33 (March-April), pp. 241-258.

Gupta, Sunil (2013), “For Mobile Devices, Think Apps, Not Ads,” Harvard Business Review, 91 (March), pp. 70-75.

Hanssens, Dominique M. (ed.) (2015), Empirical Generalizations About Marketing Impact. (Cambridge, MA: Marketing Science Institute).

Hauser, John R., Glen L. Urban, Guilherme Liberali, and Michael Braun (2009), “Website Morphing,” Marketing Science, 28 (March–April), pp. 202–23.

Havlena, William, Robert Cardarelli, and Michelle De Montigny (2007), “Quantifying the Isolated and Synergistic Effects of Exposure Frequency for TV, Print, and Internet Advertising,” Journal of Advertising Research, 47 (September), pp. 215-221.

Hoban, Paul R. and Randolph E. Bucklin (2015), “Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment,” Journal of Marketing Research, 52 (June), pp. 375-393.

Hoch, Stephen J. and Young-Won Ha (1986), “Consumer Learning: Advertising and the Ambiguity of Product Experience,” Journal of Consumer Research, 13 (September), pp. 221- 33.

Holbrook, Morris B., and Rajeev Batra (1987), “Assessing the Role of Emotions as Mediators of Consumer Responses to Advertising,” Journal of Consumer Research, 14 (December), pp. 404-420.

Holbrook, Morris B. and Elizabeth C Hirschman (1982), “The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun,” Journal of Consumer Research, 9 (Sept), pp. 132-140.

Hu, Ye, Leonard M. Lodish, and Abba M. Krieger (2007) “An Analysis of Real World TV Advertising Tests: A 15 Year Update,” Journal of Advertising Research, 47 (September), pp. 341-353.

Hwang, Heeyoung, Paul McInerney, and Jun Shin (2105), “Learning from South Korea’s Mobile-Retailing Boom, McKinsey Quarterly, May.

Jagpal, Harsharanjeet S. (1981), “Measuring Joint Advertising Effects in Multiproduct Firms,” Journal of Advertising Research, 21 (February), pp. 65-69.

Janis, Irving L. and Carl I. Hovland (1959), “An Overview of Persuasibility Research,” in C. I. Hovland and I. L. Janis (Eds.), Personality and Persuasibility (New Haven. CT: Yale University Press), pp. 1-28.

Jedidi, Kamel, Carl F. Mela, and Sunil Gupta (1999), “Managing Advertising and Promotion for Long-Run Profitability,” Marketing Science, 18 (1), pp. 1-22.

Jerath, Kinshuk, Liye Ma, and Young-Hoon Park (2014), “Consumer Click Behavior at a Search Engine: The Role of Keyword Popularity,” Journal of Marketing Research, 51 (August), pp. 480-486.

Johnson, Russell E., Chu-Hsiang Chang, and Robert G. Lord (2006), “Moving From Cognition to Behavior: What the Research Says,” Psychological Bulletin, 132 (3), pp. 381–415.

Johnson, Michael D., Andreas Herrmann, and Frank Huber (2006), “The Evolution of Loyalty Intentions,” Journal of Marketing, 70 (April), pp. 122-132.

Joo, Mingyu, Kenneth C. Wilbur, Bo Cowgill, and Yi Zhu (2014), “Television Advertising and Online Search,” Management Science, 60 (1), pp. 56-73.

Katona, Zsolt (2014), “Competing for Influencers in a Social Network,” working paper, Haas School of Business, University of California at Berkeley.

Katona, Zsolt, Peter Pal Zubcsek, and Miklos Sarvary (2011), “Network Effects and Personal Influences: The Diffusion of an Online Social Network,” Journal of Marketing Research, 48 (June), pp. 425-443.

Keller, Kevin Lane (2001a), “Building Customer-Based Brand Equity: A Blueprint for Creating Strong Brands,” Marketing Management, July/August, 15-19.


Keller, Kevin Lane (2001b), “Mastering the Marketing Communications Mix: Micro and Macro Perspectives on Integrated Marketing Communication Programs,” Journal of Marketing Management, 17 (September), pp. 819-848.

Keller, Kevin Lane (2003), “Brand Synthesis: The Multi-Dimensionality of Brand Knowledge,” Journal of Consumer Research, 29 (4), pp. 595-600.

Keller, Kevin Lane (2013), Strategic Brand Management, 4th ed., Upper Saddle River, NJ: Pearson Prentice-Hall.

Kim, Jooyoung, Hye Jin Yoon and Sun Young Lee (2010), “Integrating Advertising and Publicity,” Journal of Advertising, 39 (1), pp. 97-114.

Koslow, Scott and Gerard J. Tellis (2011), “What Scanner Panel Data Tell Us About Advertising: A Detective Story with a Dark Twist,” Journal of Advertising Research, 51 (March), pp. 87-100.

Kotler, Philip, Neil Rackham, and Suj Krishnaswamy (2006), “Ending the War between Sales & Marketing,” Harvard Business Review, July–August , pp. 68–78.

Kuehn, Alfred (1962), “Consumer Brand Choice as a Learning Process,” Journal of Advertising Research, 2 (March-April), pp. 10–17.

Kumar, Ashish, Ram Bezawada, Rishika Rishika, Ramkumar Janakiraman, and P.K. Kannan (2016), “From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior,” Journal of Marketing, 80 (January), pp. 7-25.

Lamberton, Cait and Andrew T. Stephen (this issue), “Leveraging Digital/Social/Mobile Technology,” Journal of Marketing, forthcoming.

Lambrecht, Anja and Catherine Tucker (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research,” 50 (October), pp. 561- 576.

Lawrence, Benjamin, Susan Fournier, and Frederic Brunel (2013), “When Companies Don’t Make the Ad: A Multi-Method Inquiry into the Differential Effectiveness of Consumer- Generated Advertising,” Journal of Advertising, 42 (4), pp. 292-307.

Lee, Dokyun, Kartik Hosanagar, and Harikesh S. Nair (2015), “Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook,” working paper, Tepper School of Business, Carnegie-Mellon University.

Lee, Jae Young and David R. Bell (2013), “Neighborhood Social Capital and Social Learning for Experience Attributes of Products,” Marketing Science, 32 (November-December), pp. 960– 976.

Lemon, Katherine N. and Stephen M. Nowlis (2002), Developing Synergies Between Promotions and Brands in Different Price–Quality Tiers,” Journal of Marketing Research, 39 (May), pp. 171-185.

Levy, Sydney J. (1959), “Symbols for Sale” Harvard Business Review, 37 (July-August), pp. 117-124.


Li, Hongshuang (Alice) and P.K. Kannan (2014), “Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment,” Journal of Marketing Research, 51 (February), pp. 40-56.

Lin, Chen, Sriram Venkataraman, and Sandy Jap (2013), “Media Multiplexing Behavior: Implications,” Marketing Science, 32 (March-April), pp. 310-324.

Lodish, Leonard M., Magid M. Abraham, Stuart Kalmenson, Jeanne Livelsberger, Beth Lubetkin, Bruce Richardson, and Mary Ellen Stevens (1995a), “How TV Advertising Works: A Meta-analysis of 389 Real World Split Cable TV Advertising Experiments,” Journal of Marketing Research, 32 (May), pp. 125-139.

Lodish, Leonard M., Magid M. Abraham, Jeanne Livelsberger, Beth Lubetkin, Bruce Richardson, and Mary Ellen Stevens (1995b), “A Summary of Fifty-Five In-Market Experimental Estimates of the Long-Term Effect of TV Advertising,” Marketing Science 14, (3, part 2 of 2), pp. G133-G140.

Loewenstein, George F., Elke U. Weber, Christopher K. Hsee, and Ned Welch (2001), “Risk as Feelings,” Psychological Bulletin, 127 (2), pp. 267-286.

Luo, Xueming and Naveen Donthu (2006), “Marketing’s Credibility: A Longitudinal Investigation of Marketing Communication Productivity and Shareholder Value,” Journal of Marketing 70 (October), pp. 70–91.

Lynch, John G. Jr., Howard Marmorstein and Michael F. Weigold (1988), “Choices from Sets Including Remembered Brands: Use of Recalled Attributes and Prior Overall Evaluations,” Journal of Consumer Research, 15 (September), pp. 169-184.

MacInnis, Deborah and Bernard J. Jaworski (1989), “Information Processing from Advertisements: Toward an Integrative Framework,” Journal of Marketing, 53 (October), pp. 1-23.

MacInnis, Deborah J., Christine Moorman, and Bernard J. Jaworski (1991). “Enhancing and Measuring Consumers’ Motivation, Opportunity, and Ability to Process Brand Information From Ads,” Journal of Marketing, 55 (October), pp. 32-53.

Madhavaram, Sreedhar, Vishag Badrinarayanan, and Robert E. McDonald (2005), “Integrated Marketing Communication (IMC) and Brand Identity as Critical Components of Brand Equity Strategy,” Journal of Advertising, 34 (Winter), pp. 69–80.

Malaviya, Prashant (2007), “The Moderating Influence of Advertising Context on Ad Repetition Effects: The Role of Amount and Type of Elaboration,” Journal of Consumer Research ,34 (June), pp. 32–40.

Manchanda, Puneet, Jean-Pierre Dubé, Khim Yong Goh, and Pradeep K. Chintagunta (2006), “The Effects of Banner Advertising on Internet Purchasing,” Journal of Marketing Research, 43 (February), pp. 98–108.

Mayzlin, Dina and Jiwoong Shin (2011), “Uninformative Advertising as an Invitation to Search,” Marketing Science, 30 (July-August), pp. 666-685.

McCracken, Grant (1989), “Who is the Celebrity Endorser? Cultural Foundations of the Endorsement Process,” Journal of Consumer Research, 16 (December), pp. 310-321


McGrath, John M. (2005), “A Pilot Study Testing Aspects of the Integrated Marketing Communications Concept,” Journal of Marketing Communications, 11 (3), pp.191-214.

McGuire, William J. (1978), “An Information Processing Model of Advertising Effectiveness,” in Harry L. Davis and Alvin J. Silk (Eds.), Behavioral and Management Science in Marketing, New York, NY: Ronald Press, pp. 156-180.

McLuhan, Marshall. (1964), “The Medium is the Message,” In Media and Cultural Studies: Keyworks. Meenakshi Gigi Durham and Douglas M. Kellner, eds. (2001). Malden, MA: Blackwell Publishers, Inc., pp. 129-138

Mantrala, Murali K. (2002), “Allocating Marketing Resources,” in Handbook of Marketing, B.A. Weitz and R. Wensley (editors), Sage Publications, Chapter 16.

Mela, Carl F., Sunil Gupta, and Donald R. Lehmann (1997), “The Long-Term Impact of Promotion and Advertising on Consumer Brand Choice,” Journal of Marketing Research, 34 (May), pp. 248-261.

Mitchell, Vincent-Wayne (1999) “Consumer Perceived Risk: Conceptualisations and Models,” European Journal of Marketing, 33 (1/2), pp. 163-195.

Naik, Prasad A. (2007), “Integrated Marketing Communications: Provenance, Practice and Principles,” in Handbook of Advertising, eds. Gerard J. Tellis and Tim Ambler, Sage Publications.

Naik, Prasad A. and Kay Peters (2009), “A Hierarchical Marketing Communications Model of Online and Offline Media Synergies,” Journal of Interactive Marketing, 23 (November), pp. 288-299.

Naik, Prasad A. and Kalyan Raman (2003), “Understanding the Impact of Synergy in Multimedia Communications,” Journal of Marketing Research, 40 (November), pp. 375–88.

Naik, Prasad A., Kalyan Raman, and Russ Winer (2005), “Planning Marketing-Mix Strategies in the Presence of Interactions,” Marketing Science, 24 (10), pp. 25–34.

Narayanan, Sridhar, Ramarao Desiraju and Pradeep K. Chintagunta (2004), “Return on Investment Implications for Pharmaceutical Promotional Expenditures: The Role of Marketing-Mix Interactions,” Journal of Marketing, 68 (October), pp. 90-105

Neslin, Scott (2002), Sales Promotion, MSI Relevant Knowledge Series (Cambridge, MA: Marketing Science Institute).

Nunes, Paul F. and Jeffrey Merrihue (2007), “The Continuing Power of Mass Advertising,” Sloan Management Review, Winter, pp. 63–69.

O’Guinn, Thomas, Chris Allen, Richard J. Seminik, and Angeline Close (2015), Advertising and Integrated Brand Promotion, 7th ed. (Stamford, CT: Cengage Learning).

Ohanian, Roobina (1990), “Construction and Validation of a Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness,” Journal of Advertising, 19 (3), pp. 39-52.

Oliver, Richard L. (2014), Satisfaction: A Behavioral Perspective on the Consumer. Routledge, 2014.


Olney, Thomas J., Morris B. Holbrook, and Rajeev Batra (1991), “Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time,” Journal of Consumer Research, 17 (March), pp. 440-453.

Osinga, Ernst C., Peter S.H. Leeflang, Shuba Srinivasan, and Jaap E. Wieringa (2011), “Why Do Firms Invest in Consumer Advertising with Limited Sales Response? A Shareholder Perspective,” Journal of Marketing, 75 (January), pp. 109-124.

Park, C. Whan, Deborah J. MacInnis, Joseph Priester, Andreas B. Eisingerich, and Dawn Iacobucci (2010), “Brand Attachment and Brand Attitude Strength: Conceptual and Empirical Differentiation of Two Critical Brand Equity Drivers,” Journal of Marketing, 74 (November), pp. 1-17.

Park, Ji Kyung and Deborah Roedder John (2010), “Got to Get You into My Life: Do Brand Personalities Rub Off on Consumers?,” Journal of Consumer Research 37 (December): 655– 669

Parsons, Leonard J. (1974), “An Econometric Analysis of Advertising, Retail Availability, and Sales of a New Brand,” Management Science, 20 (6), pp. 938-947.

Peterson, Robert A., & Merino, Maria C. (2003), Consumer Information Search Behavior and the Internet. Psychology & Marketing, 20(February), pp.99-121.

Petty, Richard E., John T. Cacioppo and David Schumann (1983), “Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement,” Journal of Consumer Research, 10 (September), pp. 135-146.

Pfeiffer, Markus and Markus Zinnbauer (2010), “Can Old Media Enhance New Media?,” Journal of Advertising Research, (March), pp. 42–49.

Pilotta, Joseph J., Donald E. Schultz, Gary Drenik, and Philip Rist (2004), “Simultaneous Media Usage: A Critical Consumer Orientation to Media Planning,” Journal of Consumer Behaviour, 3 (March), pp.285-292.

Punj, Girish N., and Richard Staelin (1983). “A Model of Consumer Information Search Behavior for New Automobiles.” Journal of Consumer Research, 9 (March), pp.366-380.

Raman, Kalyan and Prasaid A. Naik (2004), “Long-Term Profit Impact of Integrated Marketing Communications Program,” Review of Marketing Science, 2 (October).

Raman, Kalyan, Murali K. Mantrala, Shrihari Sridhar, and Yihui Elina Tang (2012), “Optimal Resource Allocation with Time-varying Marketing Effectiveness, Margins and Costs,” Journal of Interactive Marketing, 26 (10), pp. 43-52.

Reid, Mike, Sandra Luxton, and Felix Mavondo (2005), “The Relationship between Integrated Marketing Communication, Market Orientation, and Brand Orientation,” Journal of Advertising, 34 (Winter), pp. 11–23.

Reinartz, Werner and Peter Saffert (2013), “Creativity in Advertising: When It Works and When It Doesn’t,” Harvard Business Review, June, pp. 107-112.

Richins, Marsha L. (1997), “Measuring Emotions in the Consumption Experience,” Journal of Consumer Research, 24 (September), pp. 127-146.


Risselada, Hans and Peter C. Verhoef, and Tammo H.A. Bijmolt (2014), “Dynamic Effects of Social Influence and Direct Marketing on the Adoption of High-Technology Products,” Journal of Marketing, 78 (March), pp. 52-68.

Roberts, John (2004), The Modern Firm: Organizational Design for Performance and Growth, Clarendon Lectures in Management Studies, Oxford: Oxford University Press.

Rutz, Oliver J., Randolph E. Bucklin, and Garrett P. Sonnier (2012), “A Latent Instrumental Variables Approach to Modeling Keyword Conversion in Paid Search Advertising,” Journal of Marketing Research, 49 (June), pp. 306-319.

Rutz, Oliver J. and Randolph E. Bucklin (2011), “From Generic to Branded: A Model of Spillover in Paid Search Advertising,” Journal of Marketing Research, 48 (February), pp. 87- 102.

Schultz, Don E. and Philip J. Kitchen (1997), “Integrated Marketing Communications in U.S. Advertising Agencies: An Exploratory Study,” Journal of Advertising Research, 37 (September-October), pp. 7-18.

Schulze, Christian, Lisa Schöler, and Bernd Skiera (2014), “Not All Fun and Games: Viral Marketing for Utilitarian Products,” Journal of Marketing, 78 (January), pp. 1-19.

Schumann, Jan H., Florian von Wangenheim, and Nicole Groene (2014), “Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance Among Users of Free Web Services,” Journal of Marketing, 78 (January), pp. 59-75.

Schweidel, David A. and Wendy W. Moe (2014), “Listening In on Social Media: A Joint Model of Sentiment and Venue Format Choice,” Journal of Marketing Research, 51 (August), pp. 387-402.

Sethuraman, Raj, Gerard J. Tellis, and Richard A. Briesch (2011), “How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities,” Journal of Marketing Research, 48 (June), pp. 457-471.

Shavitt, Sharon, Carlos J. Torelli, and Jimmy Wong (2009), “Identity-based Motivation: Constraints and Opportunities in Consumer Research,” Journal of Consumer Psychology, 19 (July), pp. 261-266.

Smith, Robert E. and William R. Swinyard (1983), “Attitude-Behavior Consistency: The Impact of Product Trial versus Advertising,” Journal of Marketing Research, 20 (Aug.), 257-67.

Smith, Timothy M., Srinath Gopalakrishna, and Paul M. Smith (2004), “The Complementary Effect of Trade Shows on Personal Selling,” International Journal of Research in Marketing 21 (1), pp. 61–76.

Smith, Timothy M., Srinath Gopalakrishna, and Rubikar Chatterjee (2006), “A Three-Stage Model of Integrated Marketing Communications at the Marketing-Sales Interface,” Journal of Marketing Research, 43 (November), pp. 546–79.

Special Issue on the Emergence and Impact of User-Generated Content (2012), Marketing Science, 31 (May-June).


Stammerjohan, Claire, Charles M. Wood, Yuhmiin Chang, and Esther Thorson (2005), “An Empirical Investigation of the Interaction Between Publicity, Advertising, and Previous Brand Attitudes and Knowledge,” Journal of Advertising, 34 (Winter), pp. 55-67.

Steenkamp, Jan-Benedict E. M. and Inge Geyskens (2006), “How Country Characteristics Affect the Perceived Value of Web Sites,” Journal of Marketing 70 (July), pp. 136–50.

Stephen, Andrew T. and Jeff Galak (2012), “The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace,” Journal of Marketing Research, 49 (October), pp. 624-639.

Stephen, Andrew T., Michael R. Sciandra, and J. Jeffrey Inman (2105), “Is It What You Say or How You Say It That Matters? The Effects of Branded Content on Consumer Engagement with Brands on Facebook,” working paper, Saïd Business School, University of Oxford.

Stern, Bruce L. and Alan J. Resnick (1991), “Information Content in Television Advertising: A Replication and Extension,” Journal of Advertising Research, June/July, 36-48.

Stewart, David A. and David H. Furse (1986), Effective Television Advertising: A Study of 1000 Commercials. Lexington, MA: Lexington Books.

Swaminathan, Vanitha, Karen Stilley, and Rohini Ahluwalia (2009), “When Brand Personality Matters: The Moderating Role of Attachment Styles,” Journal of Consumer Research, 35 (April): 985–1002.

Swinyard, William R. and Michael R. Ray (1977), “Advertising-Selling Interactions: An Attribution Theory Experiment,” Journal of Marketing Research, 14 (November), pp. 509- 516

Tellis, Rajesh K. Chandy, and Pattana Thaivanich (2000), “Which Ad Works, When, Where, and How Often? Modeling the Effects of Direct Television Advertising,” Journal of Marketing Research, 37 (February), pp. 32–46.

Thompson, Debora V. and Rebecca W. Hamilton (2006), “The Effects of Information Processing Mode on Consumers’ Responses to Comparative Advertising,” Journal of Consumer Research, 32 (March), pp. 530–40.

Thompson, Debora V. and Prashant Malaviya (2013), “Consumer-Generated Ads: Does Awareness of Advertising Co-Creation Help or Hurt Persuasion?,” Journal of Marketing,77 (May), pp. 33-47.

Torelli, Carlos J., Aysegül Özsomer, Sergio W. Carvalho, Hean Tat Keh, and Natalia Maehle (2012), “Brand Concepts as Representations of Human Values: Do Cultural Congruity and Compatibility Between Values Matter?,” Journal of Marketing, 76 (July), pp. 92-108.

Trusov, Michael, Anand V. Bodapati, and Randolph E. Bucklin (2010), “Determining Influential Users in Internet Social Networks,” Journal of Marketing Research, 47 (August), pp. 643- 658.

Trusov, Michael, Randolph E. Bucklin and Koen Pauwels (2009), “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site,” Journal of Marketing, 73 (September), pp. 90-102.


61 Unnava, H. Rao and Robert E. Burnkrant (1991), “Effects of Repeating Varied Ad Executions on

Brand Name Memory,” Journal of Marketing Research, 28 (November), pp. 406-16.

Urban, Glen, Guilherme (Gui) Liberali, Erin Macdonald, Robert Bordley, and John Hauser (2014), “Morphing Banner Advertising,” Marketing Science, 33 (January-February), pp. 27- 46.

Vakratsas, Demetrios and Tim Ambler (1999), “How Advertising Works: What Do We Really Know?,” Journal of Marketing 63, (January), pp. 26-43.

Vakratsas, Demetrios, Fred M. Feinberg, Frank M. Bass, and Gurumurthy Kalyanaram (2004), “The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds,” Marketing Science, 23 (Winter), pp. 109–19.

Van Den Bulte, Christophe and Stefan Wuyts (2007), Social Networks and Marketing (Marketing Science Institute Relevant Knowledge Series, Cambridge, MA).

Van Heerde, Harald J., Maarten J. Gijsenberg, Marnik G. Dekimpe, and Jan-Benedict E.M. Steenkamp (2013), “Price and Advertising Effectiveness over the Business Cycle,” Journal of Marketing Research, 50 (April), pp. 177-193.

Voorveld, Hilde A. M., Peter C. Neijens, and Edith G. Smit (2011), “Opening the Black Box: Understanding Cross-Media Effects,” Journal of Marketing Communications, 17 (April): 69- 85.

Voorveld, Hilde A. M., Peter C. Neijens, and Edith G. Smit (2012), “The Interacting Role of Media Sequence and Product Involvement in Cross-Media Campaigns,” Journal of Marketing Communications, 18 (July), pp. 203-216.

Walker Naylor, Rebecca, Cait Poynor Lamberton, and Patricia M. West (2012), “Beyond the “Like” Button: The Impact of Mere Virtual Presence on Brand Evaluations and Purchase Intentions in Social Media Settings,” Journal of Marketing, 76 (November), pp. 105-120.

Wiesel, Thorsten, Koen Pauwels, and Joep Arts (2011), “Marketing’s Profit Impact: Quantifying Online and Off-line Funnel Progression,” Marketing Science, 30 (July-August), pp. 604-611.

Wijaya, Bambang Sukma (2012), “The Development of Hierarchy of Effects Model in Advertising,” International Research Journal of Business Studies, 5 (April–July), pp. 73-85.

Wind, Yoram and Byron Sharp (2009), “Advertising Empirical Generalizations: Implications for Research and Action,” Journal of Advertising Research, 49 (June), pp. 246-252.

Young, Antony (2010), Brand Media Strategy: Integrated Communications Planning in the Digital Era. New York: Palgrave MacMillan.

Young, Daniel R. and Francis S. Bellezza (1982), “Encoding Variability, Memory Organization and the Repetition Effect,” Journal of Experimental Psychology: Learning, Memory and Cognition, 8 (6), pp. 545-59.

Maximizing the Spread of Influence through a Social Network

Fiche de lecture  :

Kempe, D., Kleinberg, J., Tardos, E. (2013), Maximizing the Spread of Influence through a Social Network

Mots clés : approximation algorithms, social networks, viral marketing, diffusion of innovations

Les auteurs ont mis en place des processus et algorithmes pour déterminer l’étendu de l’influence sur les réseaux sociaux. Ils tentent de déterminer quels algorithmes peuvent analyser comment l’influence se diffuse sur les réseaux sociaux et comment cibler des individus

Développement :

Les réseaux sociaux jouent un rôle clé dans la diffusion de l’information, des idées et de l’influence parmis les utilisateurs.

N’importe quelle idée peut apparaître sur les réseaux sociaux et elle peut soit disparaitre très vite ou se propager et marquer l’esprit collectif de la population.

Avec le Big Data collecté sur les réseaux sociauux, ont peut estimer avec quelle force des individus s’influencent et marketer un produit afin qu’il soit adopter par une grande partie d’un dit network. Les premisces du marketing Viral et de l’Influence marketing sont que en targettant quelques personnes influentes avec un contrat ou des produits gratuits on peut déclencher une cascade d’influence ou les influenceurs vont influencé leurs followers qui vont faire de même avec leurs followers et leurs amis, ainsi de suite, créant ainsi une vague d’influence qui se propage.

Conclusion : Afin que ce phénomène se produit, il faut réussir à cibler une poignée d’influenceur qui sont en adéquation parfaite avec les thèmes de la campagne, le produit et la marque.

Il faut que les followers voient dans ce relaie du produit par l’influenceur, un acte tout à fait naturel de part l’univers de celui-ci. La correspondace doit être parfaite notamment entre la base de followers de la marque et celle de l’influenceur


Références bibliographiques

  • 1. R. Albert, H. Jeong, A. Barabasi. Error and attack tolerance of complex networks. Nature 406(2000), 378-382.
  • [2] C. Asavathiratham, S. Roy, B. Lesieutre, G. Verghese. The Influence Model. IEEE Control Systems, Dec. 2001.
  • [3] C. Asavathiratham. The Influence Model: A Tractable Representation for the Dynamics of Networked Markov Chains. Ph.D. Thesis, MIT 2000.
  • [4] F. Bass. A new product growth model for consumer durables. Management Science 15(1969), 215-227.
  • [5] E. Berger. Dynamic Monopolies of Constant Size. Journal of Combinatorial Theory Series B 83(2001), 191-200.
  • [6] L. Blume. The Statistical Mechanics of Strategic Interaction. Games and Economic Behavior 5(1993), 387-424.
  • [7] J. Brown, P. Reinegen. Social ties and word-of-mouth referral behavior. Journal of Consumer Research 14:3(1987), 350-362.
  • [8] J. Coleman, H. Menzel, E. Katz. Medical Innovations: A Diffusion Study Bobbs Merrill, 1966.
  • [9] G. Cornuejols, M. Fisher, G. Nemhauser. Location of Bank Accounts to Optimize Float. Management Science, 23(1977).
  • [10] P. Domingos, M. Richardson. Mining the Network Value of Customers. Seventh International Conference on Knowledge Discovery and Data Mining, 2001.
  • [11] R. Durrett. Lecture Notes on Particle Systems and Percolation. Wadsworth Publishing, 1988.
  • [12] G. Ellison. Learning, Local Interaction, and Coordination. Econometrica 61:5(1993), 1047-1071.
  • [13] J. Goldenberg, B. Libai, E. Muller. Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Marketing Letters 12:3(2001), 211-223.
  • [14] J. Goldenberg, B. Libai, E. Muller. Using Complex Systems Analysis to Advance Marketing Theory Development. Academy of Marketing Science Review 2001.
  • [15] M. Granovetter. Threshold models of collective behavior. American Journal of Sociology 83(6):1420-1443, 1978.
  • [16] V. Harinarayan, A. Rajaraman, J. Ullman. Implementing Data Cubes Efficiently. Proc. ACM SIGMOD 1996.
  • [17] T.M. Liggett. Interacting Particle Systems. Springer, 1985.
  • [18] M. Macy. Chains of Cooperation: Threshold Effects in Collective Action. American Sociological Review 56(1991).
  • [19] M. Macy, R. Willer. From Factors to Actors: Computational Sociology and Agent-Based Modeling. Ann. Rev. Soc. 2002.
  • [20] V. Mahajan, E. Muller, F. Bass. New Product Diffusion Models in Marketing: A Review and Directions for Research. Journal of Marketing 54:1(1990) pp. 1-26.
  • [21] S. Morris. Contagion. Review of Economic Studies 67(2000).
  • [22] G. Nemhauser, L. Wolsey. Integer and Combinatorial Optimization. John Wiley, 1988. .
  • [23] G. Nemhauser, L. Wolsey, M. Fisher. An analysis of the approximations for maximizing submodular set functions. Mathematical Programming, 14(1978), 265–294.
  • [24] M. Newman. The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2001).
  • [25] D. Peleg. Local Majority Voting, Small Coalitions, and Controlling Monopolies in Graphs: A Review. 3rd Colloq. on Structural Information and Communication, 1996.
  • [26] M. Richardson, P. Domingos. Mining Knowledge-Sharing Sites for Viral Marketing. Eighth Intl. Conf. on Knowledge Discovery and Data Mining, 2002.
  • [27] E. Rogers. Diffusion of innovations Free Press, 1995.
  • [28] T. Schelling. Micromotives and Macrobehavior. Norton, 1978.
  • [29] T. Valente. Network Models of the Diffusion of Innovations. Hampton Press, 1995.
  • [30] S. Wasserman, K. Faust. Social Network Analysis. Cambridge University Press, 1994.
  • [31] D. Watts. A Simple Model of Global Cascades in Random Networks. Proc. Natl. Acad. Sci. 99(2002), 5766-71.
  • [32] H. Peyton Young. The Diffusion of Innovations in Social Networks. Santa Fe Institute Working Paper 02-04-018(2002).
  • [33] H. Peyton Young. Individual Strategy and Social Structure: An Evolutionary Theory of Institutions. Princeton, 1998

Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids

Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids

Gerald Haübl • Valerie Trifts

Marketing Science 19, 1, 415-178


Mots clés : Consumer decision, Online shopping, Interactive decision aids,

Les possibilités offertes par internet ne sont aujourd’hui plus à démontrées et deux facteurx principaux en sont la cause :

L’augmentation drastique du nombre d’entreprises qui ont fait d’internet un superbe moyen de communiquer avec leurs clients potentiels d’une part, et l’adoption rapide par les clients d’internet en tant qu’outil d’achat ou de source d’information.

Cet article traitera particulièrement de l’achat en ligne et de l’interactivité qui découle de cet environnement.

Une des caractéristiques de l’environnement d’achat en ligne est qu’il offre au client beaucoup d’interactivité. Pour définir l’interactivité il est important de rappeler qu’elle se base sur la réciprocité et l’échange d’information, la disponibilité de l’information, la personnalisation du contenu ainsi que le feedback en temps réel.

Il est également important de bien différencier l’interactivité entres les personnes et l’interactivité entre les machines.

La première désigne comme son nom l’indique la faculté de d’interagir et de communiquer avec d’autres humains tandis que la deuxième désigne la capacité d’une personne à accéder à une base de données en ligne. C’est d’ailleurs le point central de cette étude.

Le postulat de base est que le comportement du consommateur dépend grandement du type d’interactivité proposé dans un environnement d’achat en ligne donné. Ces outils d’interactivité font ici référence à l’aide à la décision interactive mentionné dans le titre de l’article.

Ainsi, l’organisation de cet article est la suivante :

  1. Compréhension du processus de décision et de l’aide à la décision
  2. Compréhension des aides interactives dans le cadre des achats en ligne



Compréhension du processus de décision et de l’aide à la décision :

Les humains adaptent leurs stratégies de prise de décisions aux situations et à l’environnement. Payne en 1982, Shugan en 1980 et Bettman 1990 démontrent que les hommes cherchent à réduire l’effort cognitif associé à la prise de décision. Ainsi ils se conforment à une précision imparfaite de leurs décisions en retour d’une réduction de l’effort.
Ce rapport entre effort et précision entraine souvent la prise d’une décision satisfaisante. C’est particulièrement le cas lorsque les alternatives sont nuisibles ou difficiles à comparer, c’est-à-dire lorsque la compléxité de l’environnement de décision est élevée.

Ainsi lorsque l’homme fait face à ce genre d’environnement de décision complexe, il peut utiliser une technologie informatique d’aide à la décision. Ils effectuent des taches ou des fonctions de traitement d’informations distinctes. Le principe de motivation qui sous-tend les aides à la décision est que les tâches de traitement de l’information, à forte intensité de ressources, mais standard, sont effectuées par un système informatique, ce qui libère une partie de la capacité de traitement des décideurs humains. Le travail est donc partager entre l’humain et l’ordinateur.

Nous allons maintenant voir un aperçu général des aides à la décision interactive disponibles pour les consommateurs dans le but de faire des achats en ligne.

Compréhension des aides interactives dans le cadre de l’achat en ligne :

La technologie disponible pour la mise en œuvre de l’interactivité des machines dans les environnements commerciaux en ligne est une formidable opportunité pour une entreprise de pallier l’absence de contact physique avec les produits ainsi que l’absence d’interaction en face à face avec un vendeur.

Les aides à la décisions interactives peuvent prendre différentes formes allant des moteurs de recherche à vocation générale à l’agent sophistiqué. Une classification des agents d’achats interactifs repose sur la question de savoir si un outil es conçu pour aider un consommateur à déterminer ce qu’il faut acheter ou, ou il faut l’acheter.

Un phénomène bien connu concernant la prise de décision dans des environnements complexes est que les individus sont souvent incapables d’évaluer toutes les alternatives disponibles en profondeur avant de faire un choix.

Dans le cadre de la décision d’achat le processus de décision le consommateur identifie dans un premier temps un large éventail de produits pertinent sans les examiner en profondeur et un sous ensemble qui comprend les solutions de rechange les plus pertinentes. Ensuite, il évalue ce dernier en profondeur en comparant les caractéristiques des produis avant de prendre sa décision d’achat.

Ainsi dans le cadre de l’aide à la décision, les outils interactifs peuvent apporter un soutien au consommateur dans les cas suivants :

  • Le dépistage initial des produits disponibles pour déterminer ceux qui valent le plus la peine d’être examinés en premiers.
  • La comparaison approfondie des produits sélectionnés avant de prendre la décision d’achat réelle.

Dans le cas de cette étude, il a été décidé de se concentrer sur deux outils interactifs.

L’agent de recommandation : Un outil de sélection de solution de rechange :

L’agent de recommandation a pour objectif de dépister initialement des alternatives disponibles dans une boutique en ligne. La RA utilisée dans la présente étude génère une liste personnalisée d’alternatives recommandées, dans lesquelles des alternatives sont décrites par leur marque et leur nom de modèle.

Une matrice de comparaison :

La matrice de comparaison est conçue pour permettre aux acheteurs de comparer les produits plus efficacement et de manière appropriée. Il permet au consommateur d’ajouter un produit à sa propre matrice afin que ses caractéristiques soient comparées aux autres produits de cette matrice. Ainsi le consommateur peut faire son choix en se basant uniquement sur certaines caractéristiques les plus pertinentes. Le format est interactif dans la mesure ou l’homme présélectionne et la machine facilite le processus de décision d’achat. De plus la machine met l’accent sur la mémorisation des produits et facilite donc le choix du consommateur.


Une des caractéristiques de l’environnement commercial électroniques est la quasi absence de limites physiques en ce qui concerne l’affichage du produit. Ainsi un commerce en ligne offrira un nombre extrêmement élevé de solutions de rechange dans une catégorie de produits.
C’est évidemment un plus pour le consommateur qui a plus de choix que dans un magasin physique.

Néanmoins, les ressources cognitives limitées du consommateur ne lui permettent pas de traiter les quantités potentiellement importantes d’informations sur ces alternatives.

C’est la raison pour laquelle les aides à la décisions interactives dont le but est d’aider à gérer efficacement et à capitaliser sur les grandes quantités d’informations à disposition dans un environnement d’achat en ligne.

Cette étude avait donc pour but d’étudier les effets des agents de recommandation ainsi que des matrices de comparaison. Le premier aide les consommateurs dans le dépistage initial des alternatives tandis que le second facilite les comparaisons approfondies d’alternatives sélectionnées.

Ainsi l’étude menée permet de mettre en exergue l’impact important sur la quantité de recherches d’informations sur les produits, la taille et la qualité des ensemble de considérations des acheteurs et la qualité de leurs décisions d’achat.

Partant du principe bien établit qu’il existe un compromis entre effort et précision inhérent à la prise de décision humaine dans les environnements traditionnels, nous pouvons constater que des outils tel que les agents de recommandations et les matrices de comparaison permettent au consommateur d’améliorer la qualité de sa décision tout en réduisant ses efforts. De fait cette étude démontre que les outils d’aides à la décision interactifs ont un impact stratégique sur la façon dont les consommateurs recherchent des informations sur les produits et prennent des décisions d’achat.

Attitudes towards mobile advertising – A research to determine the differences between the attitudes of youth and adults.

Ünal, S., Ercis, A., & Keser, E. (2011). Attitudes towards mobile advertising – A research to determine the differences between the attitudes of youth and adults. Procedia – Social and Behavioral Sciences, 24, 361-377

Mots Clés : Consumer behavior, Mobile advertising, Consumer attitudes

Depuis le début des années 2000, la publicité sur mobile est devenue un canal de communication très important. Via ce canal mobile, les entreprises peuvent toucher le consommateur à tout moment de la journée et n’importe où. Le consommateur est toujours ouvert à la communication one-to-one qui attire plus l’attention. Parmi le canal mobile, le SMS a beaucoup de succès car il peut être interactif et personnalisé.

Dans cette étude, Ünal et al démontrent qu’il existe une différence entre l’attitude des jeunes et des adultes concernant la publicité sur mobile.

  • Dans un premier temps, nous répertorions les différentes hypothèses éventuelles sur l’impact de différents critères sur l’attitude des consommateurs face à la publicité mobile.
  • Ensuite, nous identifierons la méthodologie utilisée par Ünal et al pour mettre en valeur des différences d’attitudes entres les adultes et les jeunes concernant la publicité mobile.
  • Enfin, nous analyserons les résultats de l’étude.


Les hypothèses :

  • Hypothèse 1 : The perception de la publicité mobile comme divertissante a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 2 : La perception de la publicité mobile comme publicité informative a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 3 : La perception de la publicité mobile comme ennuyante a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 4 : La perception de la publicité mobile comme fiable a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 5 : La perception de la publicité mobile comme fiable a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 6 : La notion d’autorisation a un effet sur l’attitude des consommateurs envers la publicité mobile.
  • Hypothèse 7 : L’incitation envers la publicité mobile a un effet sur l’intention d’utiliser la publicité mobile dans la consommation.
  • Hypothèse 8 : Les attitudes envers la publicité mobile ont un effet sut l’intention d’utiliser la publicité mobile dans la consommation.
  • Hypothèse 9 : L’intention d’utiliser la publicité mobile dans la consommation a un effet d’acception ou de rejet.
  • Hypothèse 10 : Il y a des différences entre le comportement d’acceptation ou de rejet de la publicité mobile entre les jeunes et les adultes.


La méthode utilisée :

La population de l’étude inclut des utilisateurs de téléphone portable habitant à Erzurum en Turquie. Le questionnaire a été envoyé à 400 personnes. Après vérification des réponses, 380 questionnaires ont été gardés.

Il y a avait 3 groupes de questions dans ce questionnaire :

  • Groupe 1 : questions sur les caractéristiques démographiques
  • Groupe 2 : questions permettant de déterminer l’expérience des répondants par rapport à la publicité mobile.
  • Groupe 3 : questions permettant de déterminer l’attitude des répondants en réponse à la publicité mobile.

Les résultats :

Selon les résultats, une publicité mobile étant divertissante, informative, fiable, personnalisée et ayant été envoyé avec une permission a un effet positif sur les attitudes crées par la publicité mobile. Une publicité considérée comme irritante a un effet négatif sur les attitudes.

Il y a une différence entre les attitudes, les intentions et le comportement des jeunes et des adultes envers la publicité mobile. Les jeunes ont tendance à voir la publicité mobile comme plus irritante que les adultes. Contrairement aux adultes, les jeunes pensent que la publicité mobile est plus personnalisée et plus incitante. Les jeunes sont plus positifs par rapport à cette forme de publicité et sont plus susceptible de l’utiliser que les adultes.


Références :

  • Muk, Alexander. (2007), Consumers’ Intentions to Opt in to SMS Advertising: A Cross-National Study of Young Americans and Koreans, International Journal of Advertising, 26, 2, pp.177-198.
  • Tsang, Melody M., Ho, Shu-Chun. And Liang, Ting-Peng. (2004), Consumer Attitudes Toward Mobile Advertising: An Empirical Study, International Journal of Electronic Commerce, 8, 3, pp. 65–78.
  • Jun, Jong Woo. And Lee, Sangmi. (2007), Mobile Media Use And Its Impact on Consumer Attitudes Toward Mobile Advertising, International Journal of Mobile Marketing, 2, 1, pp.50-58.
  • Barutçu, Süleyman. and Göl, Meltem Öztürk. (2009), Mobil Reklamlar ve Mobil Reklam Araçlarına Yönelik Tutumlar, KMU BF Dergisi, 11, 7, pp.24-41.
  • Kolsaker, Ailsa. and Drakatos, Nikolaos. (2009), Mobile Advertising: The Influence of Emotional Attachment to Mobile Advices on Consumer Receptiveness, Journal of Marketing Communications, 15, 4., pp.267-280.
  • Wong, Mandy M.T. and Tang, Esther P.Y. (2008), Consumers’ Attitudes Towards Mobile Advertising: The Role of Permission, Review of Business Research, 8, 3, pp.181-187.
  • Yang, Kenneth C.C. (2007), Exploring Factors Affecting Consumer Intention to Use Mobile Advertising in Taiwan, Journal of International Consumer Marketing, 20(1), pp.33-49.
  • Li, Hairong. and Stoller, Brian. (2007), Parameters of Mobile Advertising A Field Experiment, International Journal of Mobile Marketing, 2, 1, pp.4-11.
  • Tetik, Hakan. (2008), Factors Affecting Consumer Attitude in Permission Based Mobile Marketing: An Empirical Study for Turkey, T.C. I, ık Üniversitesi, Sosyal Bilimler Enstitüsü, Basılmamı, Yüksek Lisans Tezi, Istanbul
  • Altuna, Oylum Korkut and Konuk, Faruk Anıl. (2009), Understanding Consumer Attitudes Toward Mobile Advertising and Its Impact on Consumers’ Behavioral Intentions: A Cross-Market Comprasion of United States and Turkish Consumers, International Journal of Mobile Marketing, 4, 2, pp.43-51.
  • Carroll, Amy., Barnes, Sutuart J., Scornavacca, Eusebio. and Fletcher, Keith. (2007). Consumer Perceptions and Attitudes Towards SMS Advertising: Recent Evidence From New Zealand, International Journal of Advertising, 26, 1, pp. 79–98.
  • Chowdhury, Humayun Kabir., Parvin, Nargis., Weitenberner, Christian. and Becker, Michael. (2006), Consumer Attitude Toward Mobile Advertising in An Emerging Market: An Emprirical Study, International Journal of Mobile Marketing, 1, 2, pp.33-42.
  • Barwise, P., Strong, C. (2002), Permission-based Mobile Advertising. Journal of Interactive Marketing, 16, 1, pp.14–24.
  • Sevtap Ünal et al. / Procedia Social and Behavioral Sciences 24 (2011) 361–377
  • Xu, David Jingjun. (2007), The Influence of Personalization in Affecting Consumer Attitudes Toward Mobile Advertising in China, Journal of Computer Information Systems, winter 2006-2007, pp.9-19.
  • Okazaki, S., Katsura, A. And Nishiyama M. (2007), How Mobile Advertising Works: The Role of Trust in Improving Attitudes and Recall, Journal of Advertising Research, 42, 2, pp.165-178.
  • Ispir, N. Bilge., Suher, H. Kemal. (2009), SMS Reklamlarına Yönelik Tüketici Tutumları, Selçuk øletiúim Dergisi, 5, 4, pp.5-17.
  • Ducoffe, Robert H. (1996), Advertising Value and Advertising on The Web. Journal of Advertising Research,vol.36, no.5, pp.21–35.
  • Rittippant, Nattharika., Witthayawarakul, Jedsada., Limpiti, Patchrabhon. and Lertdejdecha, Nathadej. (2009), Consumers’ Perception of theEffectiveness of Short Message Service (SMS) and Multimedia Message Service (MMS) as Marketing Tools, Proceedings of World Academy of Science, Engineering and Technology, 41, pp.815-821.
  • MacKenzie, Scott B. and Lutz, Richard J. (1989), An Empirical Examination of the Structural Antecedents of Attitude Toward the Ad in an Advertising Pretesting Context, Journal of Marketing, 53, 48-65.
  • Maneesoonthorn, Chadinee. and Fortin, David. (2006), Texting Behaviour and Attitudes Toward Permission Mobile Advertising: An Emprical Study of Mobile Users’ Acceptance of SMS for Marketing Purposes, International Journal of Mobile Marketing, 1, 1., pp.66-72.

La protection des données personnelles à la croisée des chemins

Mots clés : protection, données personnelles, CNIL, loi, Data, analyse, juridique.


Dans cet article, l’objectif de l’auteur est de montrer les lois en vigueurs concernant la protection des données personnelles et mettre en avant ses limites et ses axes d’améliorations.


Développement :


L’auteur nous fait tout d’abord une présentation historique des lois pour la protection des données personnelles. Il nous parle ensuite de l’intérêt de ces lois pour le consommateur qui est souvent analysé via sa data collecté sur internet par les entreprises. Il utilise le terme de marchandisation des données personnelles pour illustrer ses propos.

Ensuite il met en avant les problèmes rencontrés par les différentes lois en vigueur dans les pays comme aux Etats-Unis où les entreprises ont beaucoup de pouvoir et de émettent beaucoup de pression sur les institutions gouvernementales.

L’auteur analyse ensuite, le texte de lois le plus aboutie, qui est celui mis en place par l’union européenne. Il y a des problèmes ou des subtilités de consentement de la part du consommateur dans les différents contrats mis en place par exemple. On peut également parler de problème de proportionnalité notamment dans l’exemple de ce collège qui souhaite collecter des données sensibles sur l’ensemble des élevés.


Conclusion :


Ces observations expliquent sans doute que le principe d’une protection (improprement dite “des données personnelles” alors qu’il s’agit d’une protection des personnes à l’égard du traitement automatique des données qui les concernent) soit, dans bien des Etats, consacré au niveau constitutionnel, plus encore lorsque ces Etats ont connu des régimes autoritaires.

L’Union européenne elle-même a souhaité faire figurer la protection des données personnelles au titre des droits fondamentaux proclamés au sommet de Nice. L’exigence posée par l’article 7 de cette Charte qu’une autorité de contrôle indépendante soit instituée manifeste, sans aucun doute, le rôle qui est encore attendu de telles autorités à l’heure du “tout numérique”.

“L’organe de la conscience sociale” écrivait, il y a plus de 20 ans, Bernard Tricot en appelant de ses vœux la création d’une autorité ad hoc. Cette exigence demeure, plus que jamais, d’actualité.


Guaranty Funds, Government Shareholding and Risk Taking: Evidence from China

Mots clés : assurance, financement, remboursement, système financier, lois, gouvernement, fond de garantie, communisme.


Dans cet article, l’objectif des auteurs et de comparer le système d’assurance chinois qui est sous contrôle de l’état et celui des assureurs étrangers notamment ceux implanté en chine.


Développement :

Cet article examine les différentes hypothèses sur la subvention des risques, la surveillance et la structure de propriété en ce qui concerne les fonds de garantie en se basant sur les assurances chinoises.

Par rapport au modèle américain, les fonds chinois de garantie possèdent des caractéristiques distinctes telles que : pré-évaluation, accumulation séparée et responsabilité partielle lors de faillite. Les auteurs constatent que les risques des entreprises d’assurance diminuent suite à la création de fonds de garantie. Pour eux la pré-évaluation offre un risque limité aux assureurs et une meilleure surveillance des parties prenantes.

Les auteurs constatent également que les assureurs étrangers sont plus axés sur le risque que leurs homologues chinois contrôlés par l’État.

Conclusion :

Dans cet article les auteurs ont voulu examiné le fonctionnement des assurances chinoises et ont trouvé des similarités avec les systèmes étrangers comme un plafond de prestations pour les assurés, une évaluation forfaitaire. Mais ils y ont également trouvé des différences telles que pré-évaluation : le fonds de garantie de chaque société d’assurance opérant en Chine est évalué chaque année.

Nous montrons que la prise de risque par les entreprises d’assurance chinoises a diminué suite à la création de fonds de garantie mais que les institutions gouvernementales chinoises essayent tout de même de les contrôler.

Les assureurs contrôlés par l’État dominent le marché de l’assurance chinoise. Les régulateurs d’assurance ont toujours appliqué plus de contrôle aux assureurs contrôlés par l’État qu’aux assureurs étrangers.

Les auteurs suggèrent que les régulateurs chinois devraient surveiller les assureurs étrangers plus qu’ils ne l’ont fait à ce jour afin de créer un marché stable et concurrentiel.






Bibliographie :


Alston, L.J., Grove, W.A. and Wheelock, D.C. (1994) ‘Why do banks fail? Evidence from the 1920s’, Explorations in Economic History 31 (4): 409–431.

CrossRefGoogle Scholar

Angkinand, A. and Wihlborg, C. (2010) ‘Deposit insurance coverage, ownership and banks’ risk-taking in emerging markets’, Journal of International Money and Finance 29 (2): 252–274.

CrossRefGoogle Scholar

Ashenfelter, O. (1978) ‘Estimating the effect of training program on earnings’, Review of Economics and Statistics 60 (1): 47–57.

CrossRefGoogle Scholar

Cawley, J. and Simon, K.I. (2003) Health insurance coverage and the macroeconomy, ERIU working paper 24, Anne Arbor, MI: Economic Research Initiative on the Uninsured, from www.umich.edu/~eriu/pdf/wp24.pdf, accessed 7 December 2011.

Cheng, J., Elyasiani, E. and Jia, J. (2011) ‘Institutional ownership stability and risk taking: Evidence from the life-health insurance industry’, The Journal of Risk and Insurance 78 (3): 609–641.

Google Scholar

Cummins, D.J. (1988) ‘Risk-based premiums for insurance guaranty funds’, Journal of Finance 43 (4): 823–839.

CrossRefGoogle Scholar

Demirgüç-Kunt, A. and Detragiache, E. (2002) ‘Does deposit insurance increase banking system stability? An empirical investigation’, Journal of Monetary Economics 49 (7): 1373–1406.

CrossRefGoogle Scholar

Downs, D.H. and Sommer, D.W. (1999) ‘Monitoring, ownership and risk-taking: The impact of guaranty funds’, The Journal of Risk and Insurance 66 (3): 477–497.

CrossRefGoogle Scholar

Financial Stability Board (2012) Thematic review on deposit insurance systems, from www.financialstabilityboard.org/publications/r_120208.pdf, accessed 8 March 2013.

Gropp, R. and Vesala, J.M. (2004) Deposit insurance, moral hazard and market monitoring, from www.papers.ssrn.com/sol3/papers.cfm?abstract_id=299403, accessed 4 May 2012.

Ho, C.L., Lai, G.C. and Lee, J.P. (2013) ‘Organizational structure, board composition and risk taking in the U.S. property casualty insurance industry’, The Journal of Risk and Insurance 80 (1): 169–203.

CrossRefGoogle Scholar

Karels, G.V. and McClatchey, C.A. (1999) ‘Deposit insurance and risk-taking behavior in the credit union industry’, Journal of Banking & Finance 23 (1): 105–134.

CrossRefGoogle Scholar

Lee, S.J., Mayers, D. and Smith, C.W. (1997) ‘Guaranty funds and risk-taking: Evidence from the insurance industry’, Journal of Financial Economics 44 (1): 3–24.

CrossRefGoogle Scholar

Merton, R.C. (1977) ‘An analytic derivation of the cost of deposit insurance and loan guarantees: an application of modern option pricing theory’, Journal of Banking and Finance 1 (1): 3–11.

CrossRefGoogle Scholar

Munch, P. and Smallwood, D.E. (1980) ‘Solvency regulation in the property-liability insurance industry: Empirical evidence’, The Bell Journal of Economics 11 (1): 261–279.

CrossRefGoogle Scholar

National Conference of Insurance Guaranty Funds (2011) Review of Property and Casualty Insurance Guaranty Fund Expenses 2005–2007, Indianapolis, from www.ncigf.org/media/files/Final%20GA%20Expense%20Report%2011-28-07-3.pdf, accessed 7 March 2013.

OECD (2005) OECD guidelines on corporate governance of state-owned enterprises, from www.oecd.org/daf/ca/corporategovernanceofstate-ownedenterprises/34803211.pdf, accessed 17 December 2012.

Outreville, F.J. (1996) ‘Life insurance markets in developing countries’, The Journal of Risk and Insurance 63 (2): 263–278.

CrossRefGoogle Scholar

Penas, M.F. and Ioannidou, V.P. (2008) Deposit Iinsurance and bank risk-taking: Evidence from internal loan ratings, from www.ssrn.com/abstract=1102199, accessed 8 March 2013.

Sun, Q. (2003) ‘The impact of WTO accession on China’s insurance industry’, Risk Management and Insurance Review 6 (1): 27–35.

CrossRefGoogle Scholar

Wheelock, D.C. and Wilson, P.W. (1995) ‘Explaining bank failures: Deposit insurance, regulation, and efficiency’, The Review of Economics and Statistics 77 (4): 689–700.

CrossRefGoogle Scholar

E-commerce: 3 tendances dans l’acquisition client pour 2016


L’acquisition client a joué un rôle important dans le e-commerce en 2015 et poursuivra sa lancé l’année prochaine. Dans cet article, Knibiehly,H, VP marketing chez Twenga Solutions explique qu’acquérir de nouveaux clients est devenu primordiale alors que le marché devient de plus en plus compétitif. Grâce des centaines de fonctionnalités nouvelles sur AdWords et Google Shopping chaque année, le développement de nouvelles plateformes de shopping social, les annonceurs ont un nombre important d’outils à leur disposition mais il n’est pas pour autant plus facile de prendre de l’avance sur ses compétiteurs.

  • En premier lieu, nous traiterons du marché de la publicité en ligne puis nous terminerons par parler du Search Marketing

Dans cet article, l’auteur nous explique que  certaines plateformes auparavant non utilisées sont maintenant prises en compte dans les nouvelles stratégies marketing. En effet, Facebook, Twitter ou encore Google Shopping sont devenues essentielles pour promouvoir une marque du fait du cout moindre, du nombre de personnes touché mais aussi la possibilité de filtrer la publicité selon les profils et habitudes de consommation. L’auteur explique également que les e-commerçants utilisent de plus en plus Google Shoping qui se révèle être un outil très efficace.

De plus le Vice President marketing de chez Twenga Solutions nous explique que pour être performant en 2016, les e-commerçants devront gérer leurs campagnes sur davantage de plateformes, en prenant toujours plus de facteurs en compte. Pour ce faire, ils devront s’adapter aux trois grandes tendances suivantes :

  • La segmentation d’audience devient la norme, grâce aux mots-clés tapés dans les moteurs de recherche les annonceurs peuvent comprendre l’intention de l’utilisateur.
  • La data produit
  • Les enchères publicitaires

En conclusion les e-commerçants qui arriveront à mettre en place les différentes techniques présentées pourront prendre de l’avance sur leurs concurrents en améliorant le ROI de leurs campagnes d’acquisition client.

Viral effects of social network and media on consumers’ purchase intention

Gunawan, D. and Huarng, K. (2015). Viral effects of social network and media on consumers’ purchase intention. Journal of Business Research, 68(11), pp.2237-2241.

Keywords:  Viral marketing, eWOM, SEM, fsQCA

Main idea: This article is based on a study that explains how SNM influences people to make purchases. The research they conducted is surveys completed by people who use three SNM platforms.
The SEM results proved that SNM has no correlation with consumer’s purchasing, whereas fsQCA shows the opposite.


SNM sites are growing, and affect their user’s lives by forming connections among these users. SNM Viral marketing is often used as an electronic WOM, since a lot of people are connected and state their opinions and tastes online. The messages are transmitted way faster than they once were, which benefits the market as well as the consumers.

The study is based on three theories that make sense when put together (TRA, IAM and perceived risk). The first one is used to understand and predict behavior. The second one basically explains how one adopts to a new model, or technology. The latter clarifies the overall risk a consumer feels before and after purchasing a particular item.

The study gathered data taken from users who completed surveys they got online via these SNM sites. Based on the results, the researches would define if the theories can still apply. The findings prove that social influence is, indeed, the first leading impact.
To conclude, the SEM results indicates that social influence and the source credibility is very important and showing in consumer’s attitude toward receiving and trusting an information. Consumers are more comfortable in listening to opinions from credible sources rather than trusting arguments written with quality.


Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Milton-Keynes, England:

Open University Press / McGraw-Hill.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior.

Englewood Cliffs, NJ: Prentice-Hall.

Bauer, R.A. (1960). Consumer behavior as risk taking. In D.F. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 23–33). Boston: Harvard UniversityPress.

Beldad, A., Jong, M.D., & Steehouder, M. (2010). How shall I trust the faceless and the intangible A literature review on the antecedents of online trust. Computers in HumanBehavior, 26, 857–869.

Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance:An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.

Camarero, C., & San José, R. (2011). Social and attitudinal determinants of viral marketing dynamics. Computers in Human Behavior, 27, 2292–2300.

Cheung, C.M.K., Lee, M.K.O., & Rabjohn, N. (2008). The impact of electronic word-ofmouth.Internet Research, 18(3), 229.

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Farooq, F., & Jan, Z. (2012). The impact of social networking to influence marketing through product reviews. International Journal of Information and Communication Technology Research, 2(8), 627–637.

Fiss, P.C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420.

Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Hair, Jr, Anderson, J.F., Tatham, R.L., & Black, W.C. (1998). Multivariate data analysis (Fifthed.). Upper Saddle River, NJ: Prentice-Hall.

Hennig-Thurau, T., Gwinner, K.P., Walsh, G., & Gremler, D.D. (2004). Electronic word-ofmouth via consumer opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18, 38–52.

Ho, J.Y.C., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9), 1000–1006.

Hsu, C. -L., & Lin, J.C.-. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence, and knowledge sharing motivation. Information & Management, 45, 65–74.

Huarng, K. -H. (2014). Configural theory for ICT development. Journal of Business Research, 68, 748–756.

Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the Ninth ACM SIGKDD International Conference on

Knowledge Discovery and Data Mining (SIGKDD’03) (pp. 137–146).

Lin, H. -F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433–442.

Nunally, J. (1967). Psychometric theory. New York: Mc-Graw Hill.

Oden, N., & Larsson, R.S. (2011). What makes a marketing campaign a viral success? A descriptive model exploring the mechanisms of viral marketing. Universitat UMEA.

Petty, R.E., & Cacioppo, J.T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York, NY: Springer-Verlag.

Petty, R.E., Cacioppo, J.T., & Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of Personality and Social Psychology, 41, 847–855.

Pitta, D.A., & Fowler, D. (2005). Online consumer communities and their value to new product developers. Journal of Product & Brand Management, 14(15), 283–291.

Qiu, L., & Benbasat, I. (2005). An investigation into the effects of text-to-speech voice and  3d avatars on the perception of presence and flow of live help in electronic commerce. ACM Transactions on Computer-Human Interaction, 12(4), 329–355.

Ragin, C.C. (1987). The comparative method: Moving beyond qualitative and quantitative strategies. Berkeley: University of California Press.

Ragin, C.C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.

Rihoux, B., & Ragin, C.C. (2009). Configurational comparative methods: Qualitative Comparative Analysis (QCA) and related techniques. Thousand Oaks, CA: Sage.

Rubin, M., Watt, S.E., & Ramelli, M. (2012). Immigrants’ social integration as a function of approach-avoidance orientation and problem-solving style. International Journal of Intercultural Relations, 36, 498–505.

Smith, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial recommendations, trust, and choice in virtual markets. Journal of Interactive Marketing, 19(3), 15–37.

Stromer-Galley, J. (2004). Interactivity-as-product and interactivity-as-process. The Information Society, 20(5), 391–394.

Sussman, S.W., & Siegal, W.S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Informational System Research, 14(1), 47–65.

Vilpponen, A., Winter, S., & Sundqvist, S. (2006). Electronic word-of-mouth in online environments: Exploring referral network structure and adoption behavior. Journal of Interactive Advertising, 6(2) (http://www.jiad.org/article82).

Wang, X., Yu, C.L., & Wei, Y.J. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26, 198–208.

Weber, M. (1947). The theory of social and economic organization. NY: The Free Press.

Woodside, A.G., Eunju, K., & Tzung, C.H. (2012). The new logic in building isomorphic theory of management decision realities. Management Decision, 50(5), 765–777.

Wu, C. -W., Huarng, K.-. H., Fiegantara, S., & Pai, C.W. (2012). The impact of online customer satisfaction on the yahoo auction in Taiwan. Service Business, 6, 473–487.

Yang, H., Liu, H., & Zhou, L. (2011). Predicting young Chinese consumers’ mobile viral attitudes,intents, and behavior. Asia Pacific Journal of Marketing and Logistics, 24(1),59–77.

Zhang, Y., & Hiltz, S.R. (2003). Factors that influence online relationship development in aknowledge sharing community. Proceedings of the Ninth American Conference on InformationSystems (pp. 410–417).

Zhaveri, H. (2013). Social networking site for marketing. Proceedings of National Conference on New Horizons in IT (pp. 215–218).


Fiche de lecture  :


Mots clés : Data Science, Big Data, Data Driven decision making, 

Provst F and Fawcett T,explain how Data Science works wih Big Data. They show us how this data can be collected, used and analyzed to drive decision making.

Développement :

With vast amounts of data now available, companies in almost every industry are focused on exploiting data for competitive advantage. The volume and variety of data have far outstripped the capacity of manual analysis, and in some cases have exceeded the capacity of conventional databases.

At the same time, computers have become far more powerful, networking is ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible.

The convergence of these phenomena has given rise to the increasingly widespread business application of data science. Companies across industries have realized that they need to hire more data scientists. Academic institutions are scrambling to put together programs to train data scientists. Publications are touting data science as a hot career choice and even ‘‘sexy.’’

The authors argue that there are good reasons why it has been hard to pin down what exactly is data science. One reason is that data science is intricately intertwined with other important concepts, like big data and data-driven decision making, which are also growing in importance and attention. Another reason is the natural tendency, in the absence of academic programs to teach one otherwise, to associate what a practitioner actually does with the definition of the practitioner’s field; this can result in overlooking the fundamentals of the field.

Data-science academic programs are being developed, and in an academic setting we can debate its boundaries. However, in order for data science to serve business effectively, it is important  to understand its relationships to these other important and closely related concepts, and (to begin to understand what are the fundamental principles underlying data science.

They present a perspective that addresses all these concepts by highlighting the data science as the connective tissue between data-processing technologies and data-driven decision making.

Conclusion :

Underlying the extensive collection of techniques for mining data is a much smaller set of fundamental concepts comprising data science. In order for data science to flourish as a field, rather than to drown in the flood of popular attention, we must think beyond the algorithms, techniques, and tools in common use. We must think about the core principles and concepts that underlie the techniques, and also the systematic thinking that fosters success in data-driven decision making. These data science concepts are general and very broadly applicable

Success in today’s data-oriented business environment requires being able to think about how these fundamental concepts apply to particular business problems—to think data-analytically. This is aided by conceptual frameworks that themselves are part of data science.

Références bibliographiques

  • 1. Davenport T.H., and Patil D.J. Data scientist: the sexiest job of the 21st century. Harv Bus Rev, Oct 2012.
  • 2. Hays C. L. What they know about you. N Y Times, Nov. 14, 2004.
  • 3. Brynjolfsson E., Hitt L.M., and Kim H.H. Strength in numbers: How does data-driven decision making affect firm performance? Working paper, 2011. SSRN working paper. Available at SSRN: http://ssrn.com/abstract = 1819486.
  • 4. Tambe P. Big data know-how and business value. Working paper, NYU Stern School of Business, NY, New York, 2012.
  • 5. Fusfeld A. The digital 100: the world’s most valuable startups. Bus Insider. Sep. 23, 2010.
  • 6. Shah S., Horne A., and Capella´ J. Good data won’t guarantee good decisions. Harv Bus Rev, Apr 2012.
  • 7. Wirth, R., and Hipp, J. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 2000, pp. 29–39. DATA SCIENCE AND BIG DATA Provost and Fawcett 58BD BIG DATA MARCH 2013
  • 8. Forsythe, Diana E. The construction of work in artificial intelligence. Science, Technology & Human Values, 18(4), 1993, pp. 460–479.
  • 9. Hill, S., Provost, F., and Volinsky, C. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), 2006, pp. 256–276.
  • 10. Martens D. and Provost F. Pseudo-social network targeting from consumer transaction data. Working paper, CEDER-11-05, Stern School of Business, 2011. Available at SSRN: http://ssrn.com/abstract = 1934670.