management VOLUME 11 ■ NUMBER 3 ■ FALL 20l6 ■ ISSN 1854-4231 management issn 1854-4231 www.mng.fm-kp.si Stefan Bojnec, University of Primorska, Slovenia, stefan.bojnec@fm-kp.si executive editor Klemen Kavcic, University of Primorska, Slovenia, klemen.kavcic@fm-kp.si associate editors Imre Ferto, Corvinus University of Budapest, Hungary, imre.ferto@uni-corvinus.hu Josu Takala, University ofVaasa, Finland, josu.takala@uwasa.fi managing and production editor Alen Jezovnik, University of Primorska, Slovenia, alen.jezovnik@fm-kp.si editorial board Josef C. Brada, Arizona State University, usa josef. brada@asu.edu Birgit Burbock, FH Joanneum, Austria, birgit.burboeck@fh-joanneum.at Andrzej CieSlik, University of Warsaw, Poland, cieslik@wne.uw.edu.pl Liesbeth Dries, University of Wageningen, The Netherlands, liesbeth.dries@wur.nl Henryk Gurgul, agh University of Science and Technology, Poland, henryk.gurgul@gmail.com Timotej Jagric, University of Maribor, Slovenia, timotej.jagric@uni-mb.si Ladislav Kabat, Pan-European University, Slovakia, dekan.fep@paneurouni.com Pekka Kess, University of Oulu, Finland, pekka.kess@oulu.fi Masaaki Kuboniwa, Hitotsubashi University, Japan, kuboniwa@ier.hit-u.ac.jp Mirna Leko-Simic, Josip Juraj Strossmayer University of Osijek, Croatia, lekom@efos.hr Zbigniew Pastuszak, Maria Curie-Sklodowska University, Poland, z.pastuszak@umcs.lublin.pl Katarzyna Piorkowska, Wroclaw University ofEconomics, Poland, katarzyna.piorkowska@ue.wroc.pl Najla Podrug, University of Zagreb, Croatia, npodrug@efzg.hr Cezar Scarlat, University Politehnica of Bucharest, Romania, cezarscarlat@yahoo.com Hazbo Skoko, Charles Sturt University, Australia, hskoko@csu.edu.au Marinko Skare, Juraj Dobrila University of Pula, Croatia, mskare@efpu.hr Janez Sustersic, International School of Social and Business Studies, Slovenia, janez.sustersic@issbs.si Milan Vodopivec, University of Primorska, Slovenia, milan.vodopivec@fm-kp.si aims and scope The journal Management integrates practitioners', behavioural and legal aspects of management. It is dedicated to publishing articles on activities and issues within organisations, their structure and resources. It advocates the freedom of thought and creativity and promotes the ethics in decision-making and moral responsibility. indexing and abstracting Management is indexed/listed in ibz, doaj, Erih Plus, EconPapers, Cabell's, and ebsco. submissions The manuscripts should be submitted as e-mail attachment to the editorial office at mng@fm-kp.si. Detailed guide for authors and publishing ethics statement are available at www.mng.fm-kp.si. editorial office University of Primorska Faculty of Management Cankarjeva 5, 6101 Koper, Slovenia mng@fm-kp.si ■ www.mng.fm-kp.si published by University of Primorska Press Titov trg 4, 6000 Koper, Slovenia zalozba@upr.si ■ www.hippocampus.si & 2 P, „1Î-* Revija Management je namenjena mednarodni znanstveni javnosti; izhaja v angleščini s povzetki v slovenščini. Izid revije je finančno podprla Javna agencija za raziskovalno dejavnost Republike Slovenije iz sredstev državnega proračuna iz naslova razpisa za sofinanciranje izdajanja domačih znanstvenih periodičnih publikacij. management volume 11 (2016) number 3 issn 1854-4231 191 Marketing Management and Innovations: Challenges and Opportunities in the Marketplace; Guest Editor's Introduction to the Thematic Issue Tina Vukasovic 193 Food Innovation: The Good, the Bad and the Ugly John L. Stanton 203 Impression Management in Social Media: The Example of LinkedIn Joanna Paliszkiewicz and Magdalena Madra-Sawicka 213 Consumer Decision-Making Styles Extension to Trust-Based Product Comparison Site Usage Model Radoslaw Mafik 239 Using of Information Communication Technology Tools by the Students with Entrepreneur Intent Gregor Jagodic 255 Service Dominant Logic in Practice: Applying Online Customer Communities and Personas for the Creation of Service Innovations Adrienne Schafer and Julia Klammer 265 Abstracts in Slovene Marketing Management and Innovations: Challenges and Opportunities in the Marketplace; Guest Editor's Introduction to the Thematic Issue tina vukasovic International School of Social and Business Studies, Slovenia and University of Primorska, Slovenia tina.vukasovic@mfdps.si The increasingly changing business environment in the twenty first century, which is characterized by the consequences of demanding customers with complex value requirements, aggressive global competition, turbulent markets, rapid technological changes, and escalating globalization, has forced many firms to be innovative in all areas of business activity. Markets have become increasingly complex and hypercompetitive. Globalization and rapidly increasing innovation are drastically altering opportunities and the competitive space. Innovation, as part of the process of marketing management in enterprises, is a prerequisite for maintaining competitiveness because it can lead to the creation of an offer, which represents added value in the eyes of the customer. Innovations are not always strictly focused on products but can also be applied to processes. Companies can create competitive advantage through new ways of implementing activities on the market, that for the customer means added value, or in other words, innovation. Today, new ideas can completely transform any aspect of the value chain. Thus, the marketing function within companies is increasingly of strategic importance, as it can contribute significantly to a company's competitive edge. The role of marketing in strategic management is linked to entrepreneurship within the organization through innovation. Companies that own marketing knowledge and skills can develop unique products or services not offered by the competition. They can create a successful brand and help enhance the profitability of the company. We have to realize that innovations in products and services are just the tip of the innovation iceberg. Consequently, the efforts and resources that enterprises dedicate to introducing new sales meth- management 11 (3): 191-192 Tina Vukasovic ods into their business are currently regarded as marketing innovations and as being just as important as technological innovations when it comes to boosting companies' competitiveness. Innovations such as the ability to generate and implement new ideas in the process of marketing management in modern social and economic conditions is one of the most effective ways to generate competitive advantages to maintain position in the industry and increase market share. This thematic issue explores the links between marketing management and innovations and their challenges and opportunities in the Marketplace. It begins with a paper written by John L. Stanton who presents a quantitative measurement of the success or failure rates of new products from various food groups and examines three scenarios that might explain the lack of 'breakthrough innovation.' In the second paper, Joanna Paliszkiewicz and Magdalena Madra-Sawicka present a critical literature review of the concept of impression management and describes the strategy of self-presentation in Linkedln. In the third paper, Radoslaw Maccik describes the implementation of the concept of extended consumer decision-making styles in explaining consumer choices made in a product comparison site environment in the context of a trust-based information technology acceptance model. In the fourth paper, Gregor Jagodic looks at how which information-communication technology (ict) tools students use to help and establish their own business depends on different factors, such as their level of basics knowledge and skills, their ideas, the ease of using ict tools and also the availability of the tools (especially if they are free of charge). In the last - fifth paper, Adrienne Schaefer and Julia Klammer draw on a case study of the Swiss Federal Railways (sfr), exploring how 'value-in-context' and 'co-creation' can be put into practice. We are grateful to the organizers of the MakeLearn & tiim 2016 International Conference, which was held in Timisoara, Romania, between 25 and 27 of May 2016. All papers were put through a double-blind peer review process and the five papers that were mentioned have been accepted for this thematic issue. A special thanks go to the writers of these papers. management • volume 11 Food Innovation: The Good, the Bad and the Ugly john l. stanton Saint Joseph's University, usa jstanton@sju.edu Innovation is critical to the life of any global food company and new product development is a major activity in the innovation process. However, innovation is not always a first choice to corporate growth. This article addresses the reasons why companies may fail to innovate and provides evidence that some of these obstacles are surmountable. It is presumed, without significant evidence, that most new products fail. This research will show that the failure rate of new product development is exaggerated. It also reports that there is variation in success rate across the food categories. It will show that the strategy used to introduce new products varies significantly across the spectrum. This article will also show that the strategies used introduce new products. This research shows that there is a statistically significant difference between global regions over the 3-year period. Key words: innovation, new product development, new product success rate, and new product entry strategies Introduction Virtually no one disagrees with the idea that new products are the lifeblood of any business. But in many cases the search for new products through real innovation is often done as a last resort. The late Malcolm Forbes is attributed to have said, 'The greatest obstacle to business is success' and for most of the past, the food industry has been successful. However, two things are taking place that are likely to have a negative impact on the industry if some major changes are not made. The first is a failure to innovate. It appears that the major food companies have eschewed risk by letting entrepreneurs start food businesses and then buying the fledgling businesses. This is less risky but it is also less likely to produce long term profit and growth. And yet history shows that, in at least half of all cases, after the deal closes the acquisitions sour. There are dozens of studies and papers, and estimates of how many m&a deals fail to meet financial expectations. This can run from 50 percent to as high as 90 percent according to Jim Price of the University of Michigan (Price 2012). management 11 (3): 193-201 John L. Stanton Most research indicates that m&a activity has an overall success rate of about 50% - basically a coin toss (Sher 2012). A major cause of the failure of acquisitions as a way to innovate is the inability to integrate the new business into the existing one. The irony is that a big food company buys the innovative company and being the 'big famous company,' it tries to integrate the innovative company into the policies and procedures of the behemoth. There is a clash of cultures in which the giant usually wins and the new products fail to live up to expectations. The big losers are the big brands. A recent Ad Age article headline proclaimed 'Big Food's Big Problem: Consumers Don't Trust Brands' (Schultz 2015). A second cause of failure is that today's connected consumer is aware that the product that they fell in love with was absorbed by a big food company. Campbell Soup Company ceo Denise Morrison recently recognized this fact by saying, 'We are well aware of the mounting distrust of Big Food. We understand that increasing numbers of consumers are seeking authentic, genuine food experiences and we know that they are skeptical of the ability of large, long-established food companies to deliver them.' (Wahba 2015) Recently Kraft changed the recipe for its widely successful Macaroni & Cheese. The company wrote on its website, 'When we took the artificial flavors, preservatives and dyes out of Kraft Macaroni & Cheese, we wanted to make sure it still tasted like the Kraft Mac & Cheese you know you love. So three months ago, we quietly started selling the new recipe in our old boxes to see if you'd notice. And your silence spoke volumes.' According to this logic, I guess a consumer had to take an affirmative action and call Kraft to say you don't like its new product whereas many consumers think they would just stop buying it! The lack of innovation had led to our favorite brands turning the discovery and creation of the products consumers want over to others, while at the same time they try to find a new flavor for an old product. This is not the type of innovation that made our heritage food companies great. Innovation is not restricted to the area of food product quality but rather any area within the marketing channel. While some food companies have focused on product innovation, they have fallen short on innovating their channels of distribution and the failure to recognize major changes in the way consumers are buying food. Most major food processors are wedded to the traditional distribution channel aka supermarkets and hypermarkets. These companies do everything humanly possible to get onto the shelves of these stores includ- 604 management • volume 11 Food Innovation: The Good, the Bad and the Ugly ing kowtowing to every financial request that is made by the retailer. Now let me be clear, one should not begrudge the supermarkets, they should ask for all they want. If food processors capitulate that is their business. There are so many emerging channels of distribution that are being ignored by the food processors. For example, Amazon added 10 million new Prime customers and 60% were first time buyers this Christmas (Loeb 2014). Pharmacies, convenience stores, limited assortment stores, even office supply stores, tv shopping stores, subscription services, etc. are all selling more food, and at the same time being ignored by major food companies. Regardless of the reason, in my opinion, the lack of innovation has led to a reduction in margins and a failure to remain attractive to the new consumers. Innovation is a lot more than a line extension or a new package design. It is the major way and should be the primary way to keep corporations in sync with their consumers. To paraphrase the late Peter Drucker, there are only two functions of a firm: marketing and innovation. He didn't say a marketing department but a company dedicated to finding out what consumers want and giving it to them. By innovation he didn't mean just the r&d department. He meant a commitment to the overall direction of the company being focused on future needs as well as current ones. Ironically, reducing the efforts to be innovative was meant to cut costs and increase margins. It may have worked in the short term but many of our legacy food companies are suffering today because of cost cutting decisions made years ago especially in the area of innovation. The authors are reminded of a ceo who came to the Board of Directors with a plan to make the company a leader in their category in the near future. He had a growth plan where innovation was the primary success vehicle. He did tell the board that to get to a profitable point, the company would sustain some low profits until the changes 'kicked in.' He was fired! The next ceo sold off almost everything of value, drove up the share prices, and then left the firm. Guess what? That company is now struggling. If our legacy food processors are going to be viable in the future, they will have to be more focused on the future: future consumers, future channels of distribution and future employees. The era of fat and happy is over. The era of renewed innovation must begin in earnest. New Products and Innovation Over the years marketing managers have complained that in general their new products have failed and in many cases this was due to number 3 • fall 2016 John L. Stanton sloppy research. They would argue that rather than innovate and have failure rates estimated as high as 75% to 80% it was cheaper to simply buy successful products and let the entrepreneurs do the innovation. Recent research set out to provide an estimate of the actual failure rates of new products. In order to quantitatively measure the success or failure rates of new products, the following definitions and data were used. Product failure was simply defined by answering the question, 'Was the product available for sale and identified on the corporate website 18 months to two years after its introduction?' The new products were identified in the Mintel Global New Products Database. Since this database included information not just on the new product but also on the company and the category, it was decided to break out the categories in detail to estimate exactly what the failure rate was for each category. In order to answer the success rate question empirically, the Mintel gnpd and company websites were used. A sample of the new product introductions from 2010 through 2012 for various food groups was selected. About 1,500 new products from 8 food categories: Baby Food, Bakery, Breakfast Cereals, Chocolate Confectionery, Dairy, Desserts and Ice Cream, Fruit and Vegetables, Meals and Meal Centers were used. One of the difficulties in developing an industry standard for product success or failure is that there is no real consistency in how new product failure (or success) is defined. Each product may have a specific strategic purpose. After it serves that purpose it is removed from a product line. For example, One company used a number of new products to make a competitor's new products more difficult to introduce. As soon as the competitor's product failed, the company removed its own new product entries. It was a strategic success not a failure, even though the 'new' product was no longer on the market. Using the aforementioned definition, 66% of all new products that were sampled and reported by Mintel were successful. This is far more than the 20% to 30% that has been reported in the past. As can be seen in table 1, the success rates were also calculated for the 8 product categories separately and interestingly there is a significant variation across product categories as shown below: It was also hypothesized that there might be a relationship between product failure rates and the new product entry strategy that was used. We used the same data set for this analysis: Mintel Global New Products Database and used about 1,800 new products cases. 606 management • volume 11 Food Innovation: The Good, the Bad and the Ugly table 1 Product by Category Success Rates Food Category Success rate Baby Food Bakery Breakfast Cereals Chocolate Confectionery Dairy Desserts and Ice Cream Fruit and Vegetables Meals and Meal Centers 87.5% successful 70.9% successful 65.1% successful 78.2% successful 61.5% successful 57.6% successful 62.2% successful 70.9% successful The gnpd database defined five different types of new product entry strategies. 1. New Product. It is assigned when a new range, line, or family of products is encountered. This launch type is also used if a brand that already exists on gnpd, in one country, crosses over to a new sub-category. 2. New Variety/Range Extension. It is used to document an extension to an existing range of products on the gnpd. Think of these as line extensions or brand extensions. 3. New Packaging. This launch type is determined by visually inspecting the product for changes, and also when terms like New Look, New Packaging, or New Size are written on pack. This can include as 'new' a physically identical product in a new package. 4. New Formulation. This launch type is determined when terms such as New Formula, Even Better, Tastier, Now Lower in Fat, New and Improved, or Great New Taste are indicated on pack. 5. Relaunch. This launch type depends entirely on secondary source information (trade shows, pr, websites, press). The results as shown in table 2 indicated that 40% of all new product launches were in the 'new product' category with 59% of those being successful. The launch rate for 'new packaging' was 21% with 50% success. The results of using launch strategy 'New Variety' was 34% of all launches with 58% being successful. Product launches using 'New Formulation' were used 1.8% of the time yet firms using that strategy were successful 74% of the time. Finally, product introduction strategy 'Relaunch' was used 1.4% of time and was successful 75% of the time. This raises a separate question as to whether these results are unique to the United States or if other parts of the world use similar new product/innovation strategies. Additional analysis on different number 3 • fall 2016 197 John L. Stanton table 2 New Product Launch Strategies Strategy Count Percent region New Formulation 532 1.89 New Packaging 5989 21.27 New Product 11496 40.82 New Variety/Range Extension 9748 34.61 Re-launch 397 1.41 Total 28162 100.00 table 3 Differences in Each Region Between 2009 and 2011 Strategy usa eu k&j China New Formulation -48.70 -248.44 -381.66 118.76 New Packaging 436.38 897.99 199.71 -15.87 New Product -117.84 -1210.05 -89.52 -73.43 New Variety/Range Extension -383.01 204.23 -44.29 96.58 Re-launch 113.17 356.27 315.77 11.48 Chi-square 383.31 1029.02 902.59 33-49 parts of the world including the usa, the eu, China, Korea and Japan using the gnpd databases was conducted (Salnikova, Stanton, and Wiley 2013). Two hypotheses were tested to see if there were differences in the strategy of introducing new products. hoi There is no difference in the new product introductory positioning over a three-year period in each of the four geographic areas. H02 There is no difference in the new product introductory positioning across the four geographic areas. As can be seen in table 3, hoi is rejected as it appears that there is a statistical difference between the various countries over the 3-year period. Note that the smallest Chi square was for China which has the least amount of change in their new product introduction strategies between 2009 and 2011. It appears as if the method of introducing new products did vary over time for each country. H02 is rejected as well, as can be seen in table 4. It appears there is a significant difference between the way the four geographic areas introduce new products. The usa is using more New Packaging introductions and less New Product than expected. The eu is doing somewhat the opposite with less than expected New Packaging and New Variety/Range Extension and more New Product than expected. j&k and China seemed to be less focused on New Packaging management • volume 11 198 Food Innovation: The Good, the Bad and the Ugly table 4 Differences between Observed and Expected across the Regions Strategy usa eu j&k China New Formulation -659.29 -423.31 1431.97 -349.37 New Packaging 2605.16 -974.10 -916.07 -714.99 New Product -1637.97 4093.09 -4524.11 2068.99 New Variety/Range Extension -59.31 -2103.40 2842.80 -680.08 Re-launch -248.59 -592.28 1165.41 -324.54 with much more use of the launch types of New Formulation and New Variety/Range Extension for j&k and of New Product for China. Note, only j&k introduces more than expected of Re-launch. What does this all mean? It means the most common methods of new product introduction may not be the strategies that lead to the highest chance of new product success. It behooves each marketing manager to at least question their new product strategy and consider all the options. The other side of the coin is that while there is evidence of some successful innovation and new product development, there really doesn't appear to be the kind of innovation in the food industry that we see in the very successful technology industry. There may be more opportunities for breakthrough innovation and technology in other areas than in the food business. However, we do not see front page articles on innovation in the food industry like we do in many other industries. How much innovation has there been in other industries? Would anyone have thought that the largest chain of hotel rooms would be Airbnb, or the largest taxi cab company in the world would be Uber, or that the largest retailer would be Alibaba? Some of the changes at the ces (electronics conference) included a computer embedded in a refrigerator where you can simply push on the screen icons to order more food. However, what really seemed to be missing in the food industry is something that was just new, out-of-the-box, and exciting. Think of big innovations in the food industry, but you most likely can't think of too many. Some food companies have tried and failed. Many thought that Procter & Gamble's Olestra (artificial fat) was an absolutely breakthrough innovation. Here are a couple of things that are innovations that someday may or may not be successful. Subscription meal service may someday be a very profitable, niche business. The idea is that you can have all the ingredients as well as number 3 • fall 2016 John L. Stanton the recipe shipped to your house so that you can make the meal yourself. Making a meal is the way a cook shows their love and affection for their family or partners. There is a big difference between saying, 'Which hamburger is yours, honey?' and 'Look at what I made for you tonight, honey.' This is a big innovation because it totally changes the channels of distribution and gets people away from the traditional supermarket which is slowly dying away. Another innovation is the virtual supermarket where photos of products are shown in a variety of different venues with qr codes or the equivalent, and consumers need only to expose their cell phones to the various codes and those products will be delivered to their house. These systems have been tried in places like bus stops where consumers have time to browse the board with all the foods, or in other types of brick-and-mortar stores where the actual products do not need to be carried on a shelf in those stores. French grocer Casino undertook a first trial of its digital shopping wall in Lyon in October 2012, and Tesco South Korea caused a stir in 2011 with a qr code wall - enabling shoppers to add items to online baskets by scanning the code. Orders were then delivered that evening. Why is it that the food industry never seems to have products featured in Time magazine's new products of the year issue? Is it that the food category has already extracted all the innovation we can, or have we become so focused on the next quarterly financial report that we're not really searching for that breakthrough innovation? There are at least three scenarios that might explain the lack of 'breakthrough innovation.' One is that food is something which consumers are very comfortable and familiar. They just don't want the change. Maybe in the area of food, consumers really want small changes and not big innovations. For example, American consumers seem to be willing to try some new foods like Thai or Indian, but they won't venture into the area of proteins from insects (which many think will be an innovation in the future). The second scenario might be that the food industry is unwilling to take those big steps to create a 'breakthrough innovation.' If this scenario is true, it might be because they believe the first scenario. That is, why try to push consumers into something very new when they may be less than willing to try it? It could also be that the food industry is slowly looking for 'breakthrough innovations.' This may be a very reasonable strategy as we know Aesop told us that the tortoise beat the hare. And some companies want to be 'first at being second.' A third scenario could be that some food companies are coming 610 management • volume 11 Food Innovation: The Good, the Bad and the Ugly up with breakthrough innovations, but they are so hush-hush that the public is not aware of them. The food industry unquestionably needs innovation to survive. Whether it is the mini innovation such as line extensions of a similar brand, or larger innovations such as Amazon's 'no store' food shopping, the industry must keep up with the changing world of the consumer. Some people have suggested that in the food industry only the big strong companies survive over time. However, remember the words of Charles Darwin who said, 'In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.' Those companies that don't disappear are not necessarily large or small but rather are nimble and prepared to make the changes, and to innovate their products and their company into the changing world of the consumer. References Price, J. 2012. '6 Reasons Why So Many Acquisitions.' Business Insider, 26 October. http://www.businessinsider.com/why-acquisitions-fail -2012-10 Sher, R. 2012. 'Why Half of All m&a Deals Fail, and What You Can Do About It.' Forbes, 19 March. http://www.forbes.com/sites/ forbesleadershipforum/2012/03/19/why-half-of-all-ma-deals-fail-and-what-you-can-do-about-it/#226e1de120ae Schultz, E. J. 2015. 'Big Food's Big Problem: Consumers Don't Trust Brands; Industry Giants Shift Strategy to Win back Health-Focused Americans.' Ad Age, 25 May. http://adage.com/article/cmo-strategy/ big-food-falters-marketers-responding/298747/ Wahba, P. 2015. 'Campbell Soup ceo Says Distrust of "Big Food" a Growing Problem.' Time, 18 February. http://time.com/3714572/ campbell-soup-ceo-says-distrust-of-big-food-a-growing-problem/ Salnikova, E., J. L. Stanton, and J. Wiley 'New Product Launch Strategies: Comparing usa, eu, China, Japan and Korea.' Paper presented at the 20th International Conference on Recent Advances in Retailing and Service Science, Philadelphia, 7-10 July. This paper is published under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (cc by-nc-nd 4.0) License (http://creativecommons.org/licenses/by-nc-nd/4.c>/). number 3 • fall 2016 Impression Management in Social Media: The Example of LinkedIn joanna paliszkiewicz Warsaw University of Life Sciences, Poland joanna_paliszkiewicz@sggw.pl magdalena madra-sawicka Warsaw University of Life Sciences, Poland magdalena_madra@sggw.pl Nowadays, the relationships are often initiated and maintained in online environments, the formation and management of online impressions have gained importance and become the subject of numerous studies. The impression management is a conscious process in which people attempt to influence the perceptions of their image. They do it by controlling and managing information presented in social media. The presentation of identity is the key to success or failure for example in business life. In the article, the critical literature review related to impression management in social media is described. The example of the way of self-presentation in LinkedIn is presented. The future directions are indicated. Key words: social media, impression management, LinkedIn, social networks Introduction Social media and online community attendance have increasingly become a significant part of our social lives (Burkell et al. 2014). Social media gives new opportunity for business to contact with stakeholders, including for example job candidates (Madera 2012; Bohnert and Ross 2010). Job candidates are putting great attention to a way of presenting themselves in online communities to impress employers (Dekay 2009). Impression management in social media is becoming more important. Some researcher started to examine how self-presentations strategies affect job seekers' behaviours (van der Heide, D'Angelo and Schumaker 2012). LinkedIn has initiated a new era of workforce recruitment (Guil-lory and Hancock 2012) in which recruiters, head-hunters are screening candidates and job seekers are encouraged to create professional identities (Davison, Maraist, and Bing 2011), which will enable them to create positive impression on others (Caers and Castelyns 2011). management 11 (3): 203-212 Joanna Paliszkiewicz and Magdalena Ma,dra-Sawicka LinkedIn is the most successful network for recruiters and job seekers (Adams 2013). It enables to create business connections for establishing large, professional networks and sharing employment opportunities (Thew 2008). The aim of the article is to present a critical literature review of the concept of impression management and describe the strategy of self-presentation in LinkedIn. The Concept of Impression Management: Literature Review Managing self-presentation in online communities is an integral part of private and professional life (Rui and Stafanone 2013). When people become members of a community, they must select the relevant and appropriate pieces of information for their self-presentation to be consistent with the profile of the group. According to Schwämmlein and Wodzicki (2012) the willingness to provide personal information in member profile is high because members gain acceptance through extensive self-presentation that facilitates the establishment of relationships with other network members. The relationship between interactions and self-identity have been investigating by many researchers, for example, Goffman (1959), Jones and Pittman (1982), Leary (1996), Pontari and Schlenker (2006), and Snyder (1974). One of the first researchers who described this concept was Goffman (1959). He insisted that people not only try to convince others to see them as respectable and trustful individuals, but also they want to maintain established desired positive image. Nowadays, people do not only seek to manage their impression face-to-face but also in computer-mediated environments especially in social media (Zhao, Grasmuck, and Martin 2008). Impression management can be defined as a study of how people attempt to manage or control the perceptions which others form of them (Bozeman and Kacmar 1997; Drory and Zaidman 2007). The main aim of impression management is to steer others' impression with the use of controlling information, photos, and videos and present them in a proper way in social media. In real life, the impression management takes place through both verbal and nonverbal communication, including body language, posture, speech and rank (i.e., status) (Bolino and Turnley 1999; Leary and Kowalski 1990). Both in real life and online, self-representation connects the idea of who we are to the outside world (Rosenfeld, Giacalone, and Riordan 2002). Thanks to the feedback from recipients, people can explore management • volume 11 Impression Management in Social Media their presented images and develop or adjust it to the desired images. Impression management is recognized as the key element of successful communication with co-workers, team members, and colleagues. According to Gardner and Avolio (1998), it can help managers who are charismatic leaders to achieve an authentic self-representation, allowing them to increase their trustworthiness, credibility, esteem and power (Jung and Sosik 2003). Impression management is used in individual life as also can be applied at the organizational level (Avery and McKay 2006; Mohamed and Gardner 2004). Organizations use impression management in social media to build the positive image. The impression management model consists of the two key players: an 'actor' who engages in 'impression management behaviours' and an 'audience' who interacts with 'actors' under certain 'environmental settings'. This actor - audience relationship often occurs between managers and their subordinates (Barsness, Diekmann, and Seidel 2005). Impression management can by divided into two strategies: assertive (which an actor uses to establish desirable image), and protective (which are excuses and justifications to repair damaged identities) (Drory and Zaidman 2007). Very interesting taxonomy of organizational impression management was presented by Mohamed, Gardner, and Paolillo (1999). According to them, it can be categorized into four types: direct and assertive, direct and defensive, indirect and assertive, and indirect and defensive tactics. Organizations use (Mohamed, Gardner, and Paolillo 1999): • direct tactics to present information about their skills, abilities, and accomplishments, • indirect tactics to enhance their images by managing information about the people and events with which they are associated with, • utilize assertive tactics when they see opportunities to boost their image, • defensive tactics to minimize or repair damage to their images. Members of social networks have various socio-discursive needs -expressive, communicative, or promotional. People engage in self-presentation for many social and professional reasons, including gaining employment, to conduct business, to establish friendships, to express themselves (Shepherd 2005; Bolino et al. 2008) or to correct inaccurate impressions that colleagues have of them (Giacalone and Rosenfeld 1991). The topic of self-presentation was examined by number 3 • fall 2016 Joanna Paliszkiewicz and Magdalena Ma,dra-Sawicka Birnbaum (2013), DeAndrea and Walther (2011), Labrecque, Markos, and Milne (2011), and Schwämmlein and Wodzicki (2012). The importance of being recognized in a positive light has become very important in social circles. The development of social media like LinkedIn has facilitated identity construction through the abilities to shape the information, photos, and video posted on an individual's profile in attempts to control how others will perceive them in real and internet world. Goffman (1959) highlighted the fact that the perception of others socially influences our behaviour. Impression Management Strategy: The Example of LinkedIn LinkedIn is a social network dedicated to professionals and is focused on business relationships and interactions. It has more than 364 million members in over 200 countries. LinkedIn was launched on May 5, 2003. Its founders are Reid Hoffman, Allen Blue, Konstantin Guericke, Eric Ly, and Jean-Luc Vaillant (see https://press .linkedin.com/about-linkedin). LinkedIn can be used to build awareness and gain referrals (Kietzmann et al. 2011; Mas-Bleda et al. 2014). It is very popular among recruiters and job seekers. This platform is used to find jobs, recommend others in the network, and receive recommendations from others. LinkedIn allows users to fill in information, which includes profile summary, experience, volunteer experience and causes, projects, languages, certifications, publications, education, discussion posts and comments, recommendations, endorsed skills and expertise, interests, honours and awards, and contact information. A properly filled-out profile gives information about one's job title, detail employment history, business accomplishments, and where they were educated. On the profile, individual can also present a portrait (photo). There are also presented the recommendations from others. People can join groups, especially those offering jobs, recruitment, and business deals. LinkedIn enables to share Amazon book-reading lists, slide presentations, documents, travel itineraries, and blog entries. LinkedIn helps to find people based on a variety of criteria for example: by defining the specific industry, the size of the company, the seniority level, and the groups of which a particular person is a member (Bradbury 2011). LinkedIn is giving a lot of opportunities for people who are looking for new challenges. A lot of companies use it for talent acquisi- management • volume 11 Impression Management in Social Media tion. The impression management is very important concept related to this social network. Tsai et al. (2011) and Guillory and Hancock (2012) found that a job seeker can influence recruiter evaluations through impression management. Individuals use various self-presentation tactics in social media to present themselves in favourable ways. The self-presentation tactics can be described as: 'behaviours used to manage impressions to achieve foreseeable short-term interpersonal objectives or goals' (Lee et al. 1999, 702). LinkedIn presents information about a user that viewers can use to make judgments about the source, such as their credibility, trustfulness, social and professional attractiveness. One must fill in all information in the LinkedIn profile to create a positive image (Ivcevic and Ambady 2012). It is important to put the photography. Results of the research presented by Edwards et al. (2015) indicates that users who post a profile picture along with their LinkedIn profile are perceived as more socially attractive and more competent than users who do not post a picture. Images help to increase social presence in electronic communications. Neuberg and Fiske (1987) highlights that the appearance of an individual is most prominent when we first come into contact with somebody. However, the more information a person has on the profile, the more likely hiring professionals will gain an understanding of the individual's personality and behaviour looking at this information. Also, the information is people's interests and hobbies. According to Goffman's (1959) theory of identity management, people strategically present characteristics that they believe others will approve. For example, people who are looking for a job may be aware that posting specific interests personal or professional can sway how attractive they are to recruiters because they may coincide with recruiters' hobbies. Spelling and grammar mistakes on the LinkedIn Profile are believed to be more troubling, than on paper cv, because it can very fast create a negative impression. And for example, recruiters may dismiss a candidate based on a single spelling error. Another important characteristic of the LinkedIn profile is the number of connections users have in their network. The number of connections is important for a candidate in certain careers (i.e. sales, marketing, public relations, recruiting, etc.). It also shows how this person can create a social network. For good impression management also is important to put attention to defining the skill set. Social network LinkedIn users are asked not to provide their life story number 3 • fall 2016 Joanna Paliszkiewicz and Magdalena Ma,dra-Sawicka but to highlight specific skills, thus promoting their strengths for different business stakeholders. LinkedIn users, who are not writing a comprehensive list of skills and expertise, will be found less often than those members who do list them. The use of LinkedIn, as a recruitment tool is very popular because it is easy to manage and it is a low-cost solution (Zide, Elman, and Shahani-Denning 2014; Chiang and Suen 2015). Potential candidates from all over the world are easier to find. There are opportunities for introduction to new professional connections through existing networks. Conclusion and Future Directions LinkedIn is a social networking site dedicated to making business connections for building a professional network and sharing employment opportunities. The users generate the content, and they can professionally prepare the information which they would like to publish on the Internet. Nowadays, it is very important to have knowledge about the strategy of self-presentation in social networks because the presentation of identity is the key to success or failure for example in business life. Summarize, it is very important to create professional LinkedIn Profile in which user will: • build a complete profile, • highlight only relevant information, • always include photography, • limit recommendations to trustful people, • add credible people to network, • join different groups, and • provide high-quality and researched-based information. People should be conscious and active in impression management and be aware what information exists about them in social media and if the information is protected by the appropriate levels of security and privacy. The social networks are growing, and there is a need for further research to provide more definitive guidelines on both the potential advantages as well as disadvantages of using professional networks. Although using LinkedIn for recruitment purposes is commonplace in today's workforce there is a need to solve many ethical and legal problems related to use private information published in social media in the process of recruitment and selection. Privacy in social media is critical issue that deserves reflection and research, especially cyberstalking and location disclosure, social profiling and third management • volume 11 Impression Management in Social Media party disclosure, invasive privacy agreements. Questions that need attention regarding this privacy issue are: Will social media sites be honest and competent in dealing with users' information? Will social media be capable in preventing users against cyberstalking, location disclosure, social profiling? References Adams, S. 2013. 'New Survey: LinkedIn More Dominant Than Ever among Job Seekers and Recruiters, But Facebook Poised to Gain.' Forbes, 5 February. http://www.forbes.com/sites/susanadams/2013/ 02/05/new-survey-linked-in-more-dominant-than-ever-among -job-seekers-and-recruiters-but-facebook-poised-to-gain/ #7f6d6ib8i6bf Avery, D. R., and P. F. McKay. 2006. 'Target Practice: An Organizational Impression Management Approach to Attracting Minority and Female job Applicants.' Personal Psychology 59:157-87. Barsness, Z. I., K. A. Diekmann, and M. D. L. Seidel. 2005. 'Motivation and Opportunity: The Role of Remote Work, Demographic Dissimilarity, and Social Network Centrality in Impression Management.' Academy of Management Journal 48:401-19. Birnbaum, M. G. 2013. 'The Fronts Students Use: Facebook and the Standardization of Self-Presentations.' Journal of College Student Development 54 (2): 155-71. Bohnert, D., and W. H. Ross. 2010. 'The Influence of Social Networking Web Sites on the Evaluation of Job Candidates.' Cyberpsychology, Behavior and Social Networking 13 (3): 341-47. Bolino, M. C., K. M. Kacmar, W. H. Turnley, and J. B. Gilstrap. 2008. 'A Multilevel Review of Impression Management Motives and Behaviors.' Journal of Management 34:1080-109. Bolino, M. C., and W. H. Turnley. 1999. 'Measuring Impression Management in Organizations: A Scale Development Based on the Jones and Pittman Taxonomy.' Organizational Research Methods 2 (2): 187-206. Bozeman, D. P., and K. M. Kacmar. 1997. 'A Cybernetic Model of Impression Management Processes in Organizations.' Organizational Behavior and Human Decision Process 69 (1): 9-30. Bradbury, D. 2011. 'Data Mining with LinkedIn.' Computer Fraud and Security, no. 10: 5-8. Burkell, J., A. Fortier, L. Y. C. Wong, and J. L. Simpson. 2014. 'Facebook: Public Space, Or Private Space?' Information, Communication and Society 19 (1): 1-12. Caers, R., and V. Castelyns. 2011. 'LinkedIn and Facebook in Belgium: The Influences and Biases of Social Network Sites in Recruitment and Selection Procedures.' Social Science Computer Review 29 (4): 437-48. number 3 • fall 2016 Joanna Paliszkiewicz and Magdalena Ma,dra-Sawicka Chiang, J. K.-H., and S.-Y. Suen. 2015. 'Self-Presentation and Hiring Recommendations in Online Communities: Lessons from LinkedIn.' Computers in Human Behavior 48:516-24. Davison, H. K., C. Maraist, and M. N. Bing. 2011. 'Friend or Foe? The Promise and Pitfalls of Using Social Networking Sites for hr Decisions.' Journal of Business Psychology 26 (2): 153-9. DeAndrea, D. C., and J. B. Walther. 2011. 'Attributions for Inconsistencies between Online and Offline Self-Presentations.' Communication Research 38 (6): 805-25. Dekay, S. 2009. 'Are Business-Oriented Social Networking Web Sites Useful Resources for Locating Passive Jobseekers? Results of a Recent Study.' Business Communication Quarterly 72 (1): 101-5. Drory, A., and N. Zaidman. 2007. 'Impression Management Behavior.' Journal of Managerial Psychology 22:290-308. Edwards C., B. Stoll, N. Faculak, and S. Karman. 2015. 'Social Presence on LinkedIn: Perceived Credibility and Interpersonal Attractiveness Based on User Profile Picture.' Online Journal of Communication and Media Technologies 5 (4): 102-15. Gardner, W. L., and B. J. Avolio. 1998. 'The Charismatic Relationship: A Dramaturgical Perspective.' Academy of Management Review 23 (1): 32-58. Giacalone, R. A., and P. Rosenfeld. 1991. Applied Impression Management: How Image Making Affects Managerial Decisions. Newbury Park, ca: Sage. Goffman, E. 1959. The Presentation of Self in Everyday Life. New York: Anchor Books. Guillory, J., and J. T. Hancock. 2012. 'The Effect of LinkedIn on Deception in Resumes.' Cyberpsychology, Behavior and Social Networking 15 (3): 135-40. Ivcevic, Z., and N. Ambady. 2012. 'Personality Impressions from Identity Claims on Facebook.' Psychology of Popular Media Culture 1 (1): 3845. Jones, E. E., and T. S. Pittman. 1982. 'Toward a General Theory of Strategic Self-Presentation.' In Psychological Perspectives on the Self, edited by J. Suls, 1:231-62. Hillsdale, nj: Erlbaum. Jung, D. I., and J. J. Sosik. 2003. 'Group Potency and Collective Efficacy Examining Their Predictive Validity, Level of Analysis, and Effects of Performance Feedback on Future Group Performance.' Group and Organization Management 28 (3): 366-91. Kietzmann, J. H., B. S. Silvestre, I. P. McCarthy, and L. F. Leyland. 2012. 'Unpacking the Social Media Phenomenon towards a Research Agenda.' Journal of Public Affairs 12 (2): 109-19. Labrecque, L. I., E. Markos, and G. R. Milne. 2011. 'Online Personal Branding: Processes, Challenges, and Implications.' Journal of Interactive Marketing 25 (1): 37-50. management • volume 11 Impression Management in Social Media Leary, M. R. 1996. Self-Presentation: Impression Management and Interpersonal Behavior. Boulder, co: Westview. Leary M. R., and R. M. Kowalski. 1990. 'Impression Management: A Literature Review and Two-Component Model.' Psychological Bulletin 107:34-47. Lee, S., B. M. Quigley M. S. Nesler, A. B. Corbett, and J. T. Tedeschi. 1999. 'Development of a Self-Presentation Tactics Scale.' Personality and Individual Differences 26:701-22. Madera, J. M. 2012. 'Using Social Networking Websites As a Selection Tool: The Role of Selection Process Fairness and Job Pursuit Intentions.' International Journal of Hospitality Management 31 (4): 127682. Mas-Bleda, A., M. Thelwall, K. Kouska, and I. F. Aquillo. 2014. 'Do Highly Cited Researchers Successfully Use the Social Web?' Scien-tometrics 101:337-56. Mohamed, A. A., and W. L. Gardner. 2004. 'An Exploratory Study of In-terorganizational Defamation: An Organizational Impression Management Perspective.' Organizational Analysis 12:129-45. Mohamed, A. A., W. L. Gardner, and J. G. P. Paolillo. 1999. 'A Taxonomy of Organizational Impression Management Tactics.' Advances in Competitiveness Research 7 (1): 108-30. Neuberg, S., and S. Fiske. 1987. 'Motivational Influences on Impression Formation: Outcome Dependency Accuracy-Driven Attention, and Individuating Processes.' Journal of Personality and Social Psychology 53 (3): 431-44. Pontari, B. A., and B. R. Schlenker. 2006. 'Helping Friends Manage Impressions: We Like Helpful Liars But Respect Nonhelpful Truth Tellers.' Basic and Applied Social Psychology 28:177-83. Rosenfeld, P., R. Giacalone, and C. Riordan. 2002. Impression Management: Building and Enhancing Reputations. London and New York: Routledge. Rui, J. R., and M. A. Stefanone. 2013. 'Strategic Self-Presentation Online: A Crosscultural Study.' Computers in Human Behavior 29 (1): 110-8. Schwämmlein, E., and K. Wodzicki. 2012. 'What to Tell about Me? Self-Presentation in Online Communities.' Journal of Computer-Mediated Communication 17 (4): 387-407. Shepherd, I. D. H. 2005. 'From Cattle and Coke to Charlie: Meeting the Challenge of Self Marketing and Personal Branding.' Journal of Marketing Management 21 (5): 589-606. Snyder, M. 1974. 'Self-Monitoring of Expressive Behavior.' Journal of Personality and Social Psychology 30:526-37. Thew, D. 2008. 'LinkedIn - A User's Perspective: Using New Channels for Effective Business Networking.' Business Information Review 25 (2): 87-90. number 3 • fall 2016 211 Joanna Paliszkiewicz and Magdalena Ma,dra-Sawicka Tsai, W. C., N. W. Chi, T. C. Huang, and A. J. Hsu. 2011. 'The Effects of Applicant Resume Contents on Recruiters' Hiring Recommendations: The Mediating Roles of Recruiter Fit Perceptions.' Applied Psychology: An International Review 60 (2): 231-54. Van Der Heide, B., J. D. D'Angelo, and E. M. Schumaker. 2012. 'The Effects of Verbal versus Photographic Self-Presentation on Impression Formation in Facebook.' Journal of Communication 62 (1): 98120. Zhao, S., S. Grasmuck, and J. Martin. 2008. 'Identity Construction on Facebook: Digital Empowerment in Anchored Relationships.' Computer in Human Behavior 24:1816-36. Zide, J., B. Elman, and C. Shahani-Denning. 2014. 'LinkedIn and Recruitment: How Profiles Differ across Occupations.' Employee Relations 36 (5): 583-604. This paper is published under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (cc by-nc-nd 4.0) License (http://creativecommons.org/licenses/by-nc-nd/4.Q/). management • volume 11 Consumer Decision-Making Styles Extension to Trust-Based Product Comparison Site Usage Model radoslaw macik Maria Curie-Sktodowska University, Poland radoslaw.macik@umcs.pl The paper describes an implementation of extended consumer decision-making styles concept in explaining consumer choices made in product comparison site environment in the context of trust-based information technology acceptance model. Previous research proved that trust-based acceptance model is useful in explaining purchase intention and anticipated satisfaction in product comparison site environment, as an example of online decision shopping aids. Trust to such aids is important in explaining their usage by consumers. The connections between consumer decision-making styles, product and sellers opinions usage, cognitive and affective trust toward online product comparison site, as well as choice outcomes (purchase intention and brand choice) are explored trough structural equation models using pls-sem approach, using a sample of 461 young consumers. Research confirmed the validity of research model in explaining product comparison usage, and some consumer decision-making styles influenced consumers' choices and purchase intention. Product and sellers reviews usage were partially mediating mentioned relationships. Key words: consumer decision-making styles, online product comparison site usage, cognitive and affective trust, products/sellers reviews, purchase intention, pls-sem Introduction Paper goal is to extend trust-based acceptance model in the context of online product comparison site by including consumer decision-making styles concept and brand choice on the example of the simulated choice of an automatic coffee machine in a quasi-experimental setting. The sample of 461 young consumers participated in an online quasi-experimental setting. As mentioned extensions were not previously analysed, research made is exploratory in nature. The main research question is which and how consumer decision-making styles influence trust toward product comparison site usage and product brand choice. Data analysis utilises pls-sem approach, management 11 (3): 213-237 Radoslaw Majcik including pls-mga (multi-group analysis) for main chosen brands, as a way to explore postulated relationships. The paper is organised in ten parts. After the short introduction, the online product comparison sites mechanics and business models are presented, with the description of trust-based technology adoption model and introduction to the concept of consumer decision-making styles. Next, conceptual research model with research questions is introduced, followed by detailed description of used sample and measures (with reliability and validity assessment). Results part is organised along main research model, its estimates, and multi-group comparisons regarding groups for main chosen brands. Obtained results are discussed in next part of the paper, ending with research implications, limitations of the study, and conclusion. Contemporary Online Product Comparison Sites Common access to online shopping changed buying habits of many consumers in last 20 years. The share of online retail spending (on goods) increased over the time with 15-18 percentage points growth y-o-y, up to over 10% share in total retail on mature markets as United Kingdom (the leader with about 15% share), United States or Germany (see http://www.retailresearch.org/onlineretailing.php). This involves a large number of decisions to find products and sellers online, with two most common alternative approaches: • the choice of the well-known online store brands (like Amazon, Zalando etc.) or places where someone previously bought with satisfaction (without comparison of sellers), or, • finding the best deal - often with the help of product comparison sites. The second strategy is in the scope of presented research. Contemporary online product comparison sites evolved from simple price comparison engines introduced nearly 20 years ago. They are also working under different business model than their predecessors. Product comparison engines are working as infomediaries typically in business model assuming paid integration (via api and parsing structured xml file with offers data) of particular vendor offers with comparison engine. Solutions using bots crawling the net to seek online store and their assortment to include in comparison engine without payment and direct integration are nowadays rare -even Twenga makes possible shop integration for a fee. In both cases, the mechanics of product comparison site working is to aggregate information from product comparison agent (or 624 management • volume 11 Consumer Decision-Making Styles bot), that is configured to gather product information (such as actual price, product availability, product description etc.) from online vendors and/or product information databases. As consumer interacts with product comparison site, typically having recognizable brand, he/she is not interested about underlying technology (allowing the site to present demanded information on request) and/or nature of commercial agreements between comparison site and online vendor, this suggests that product comparison agent should be transparent to the comparison site user. Aggregated information retrieved on online shopper request is revealed to him/her in the form of ranking. By interacting with product comparison site consumers leave some traces of their behaviour, that are valuable for online vendors and comparison sites for their marketing activities. Figure 1 shows the flows between online vendor, product comparison site, and consumer. Product comparison sites are nowadays enhanced by opinions from consumers about products and sellers (possibly so called 'trusted opinions' of non-anonymous for the site users who bought a particular product). Those opinions are usually presented as average ratings - particularly for sellers' credibility and detailed pieces of text. Young consumers are more innovative toward information technology usage. They also are using online decision shopping aids including mobile tools more often and in the more extensive way (Ma-cik and Nalewajek 2013), so studying this group behaviour can be useful to make predictions by analogy for consumers later accepting new technologies. Previous research also suggests the power of online opinions and reviews for this group of consumers (Nalewajek and Mapk 2013). The influence of online reviews on purchasing behaviour has been confirmed by many studies in the information systems and consumer behaviour fields (e.g., Forman, Ghose, and Wiesenfeld 2008; Kham-mash and Griffiths 2011). Typically the effect of positive and negative reviews for particular e-commerce site have been studied, and product reviews have been left from detailed consideration. Negative reviews are believed to have a stronger effect on consumer decisions than positive ones (Park and Lee 2009), as being more diagnostic and informative (Lee, Park, and Han 2008). Typically the consumer using product comparison site faces with a mix of positive and negative product reviews and seller opinions, this is known as inconsistent reviews setting (Tsang and Prendergast 2009). For this study, both types of opinions have been used: about products and about sellers. number 3 • fall 2016 Radoslaw Majcik figure 1 Flows Between Online Vendor, Product Comparison Site and Consumer: Simplified Approach (numbers represent steps of flows between ecosystem members; own elaboration, loosely based on concept of Wan, Menon, and Ramaprasad (2007, 66)) Focus was on declared number of opinions read more or less precisely, leaving out of consideration their negative or positive connotations, under the assumption: the more opinions read, the greater trust to product comparison site. Trust-Based Acceptance Model Numerous research studies show that trust toward online business is a key driver for the success of e-commerce (Cheung and Lee 2006; Hong and Cho 2011), particularly for online retailers (Kim and Park 2013). Many studies researching consumer trust toward e-commerce site are following Komiak and Bensabat (2006) trust-based acceptance model built upon widely used in e-commerce studies theory of reasoned action (tra) (Hoehle, Scornavacca, and Huff 2012; Komiak and Benbasat 2006). According to tra individual's behaviour is pre- 626 management • volume 11 Consumer Decision-Making Styles dicted by his/her behavioural intention, while behavioural intention is formed as an effect of attitude, beliefs, and subjective norms (Fish-bein and Ajzen 1975). Those connections are causal relationships, so they are typically modelled using sem approach. Komiak and Benbasat (2006) developed mentioned trust-based acceptance model for explaining the adoption of online recommendation agents. They examined two types of trust in the model: cognitive trust and affective trust. Cognitive trust is conceptualized as trusting beliefs while affective trust should be considered as a form of trusting attitude. In online environments, consumers often affectively evaluate trusting behaviour. High affective trust suggests having favourable feelings toward performing the behaviour. The trust-based acceptance model highlights that cognitive trust affects emotional trust, which further leads to individuals' adoption intention (Komiak and Benbasat 2006). This is convergent with tra approach when adoption process resembles the following sequence: belief 'attitude' intention, although the subjective norm is the construct dropped in trust-based acceptance model as adoption behaviour is considered as voluntary rather than mandatory according to Komiak and Benbasat (2006). Cognitive trust can be analysed in three main categories: competence, benevolence, and integrity as suggest McKnight, Choudhury, and Kacmar (2002). Trust in competence refers to the extent to which consumers perceive an online retailer or service provider as having skills and abilities to fulfil what they need (Mayer, Davis, and Schoor-man 1995). Trust in benevolence is consumers' perception that the retailer/service provider will act in their interest (Hong and Cho 2011). Trust in integrity refers to consumers' perception about honesty and promise-keeping by online retailer/service provider (McKnight, Choudhury, and Kacmar 2002). Those concepts are used in this research in the context of product comparison engine usage. Consumer Decision-Making Styles A consumer decision-making style concept is defined as 'a mental orientation characterizing a consumer's approach to making choices' (Sproles and Kendall 1986, 268), and consumer decision-making styles can be perceived as 'basic buying-decision making attitudes that consumers adhere to, even when they are applied to different goods, services or purchasing decisions' (Walsh et al. 2001, p. 121). Consumer decision-making styles are connected to consumer personality, and research suggest that they are relatively stable constructs (Sproles and Kendall 1986; Lysonsky, Durvasula and Zotos number 3 • fall 2016 Radoslaw Majcik table 1 Description of Consumer Decision-Making Styles: Extended version Style name/short name Description Perfectionistic perf Sensitive to high quality products, prone to spend money and/or time to get the expected quality expecting customer care, thoroughly comparing the available options Brand-Conscious bc Believing that price of branded products is appropriate to their quality, buying well-known and heavily advertised brands, often in shopping malls and specialty stores Novelty Fashion Conscious nfc Willing to put extra effort to obtain a trendy, new products sooner than others; follower of fashion, always in line with current trends, often buys due variety-seeking motives Recreational Shopping Conscious rsc Hedonistic, perceiving shopping environment as pleasant and desirable, spending much time on shopping Price-Value Conscious pvc Prone for getting highest possible 'value for money' - sensitive to price reductions, looking for low prices, often carefully comparing products before purchase, rarely buys cheapest products Impulsive imp Relying on impulse to buy does not plan purchases, not paying much attention to how much is spending, prone for buying on sales Confused by Overchoice co Feels the fatigue of to many products, brands and shopping options, often has trouble in deciding Habitual Brand-Loyal hbl Has strong habits for buying specific brands and/or at the same places Compulsive comp Having tendency to uncontrolled spending, and addiction for shopping (style added by author) Ecologically Aware eco Prone to choose products that are ecologically safe for him/her and for environment (style added by author) notes Own elaboration, including early insights by Sproles and Kendall (1986). 1995). Particular shopping activities and attitudes toward shopping can be perceived as direct outcomes of consumer's decision-making styles (Tai 2005), and tendencies revealed in particular person styles profile are modified in particular shopping process by situational factors. Consumer decision-making concept has been used in several contemporary studies (Walsh et al. 2002; Tai 2005), and proved to be useful to explain outcomes of particular shopping activities and attitudes toward shopping, including usage of online channel (Mapk and Macik 2009). Consumer decision-making styles are measured typically via pcs (Profile of Consumer Style) questionnaire proposed by Sproles and Kendall (1986). In this research extension and reconstruction of pcs has been used, with 2 new styles have been added on the base of pre- 628 management • volume 11 Consumer Decision-Making Styles |rq2 |rq3 | Consumer Decision-Making Styles (extended to 10 styles) \ figure 2 Conceptual Research Model vious author research. In result 10 styles (including original 8) were measured by 30 items scaled as Likert-type scale with five variants of answers (short form of reconstructed by Mapk and Maccik (2015) pcs scale named spdzi4k). Those styles are described in greater detail in table 1. Listed styles are forming personal profile consumer decision-making styles - particular person possesses an individual combination of them, when all styles are manifesting itself on different levels, with some styles more intense or prominent (Sproles and Kendall 1986). Conceptual Research Model and Research Questions Mentioned concepts of trust-based adoption model and consumer decision-making styles putted in context of online product comparison sites usage were leading to propose conceptual model (figure 2). In this approach gained with time experience in online product comparison sites usage and opinions about products and sellers are antecedents for cognitive and affective trust for online product comparison site according to trust-based adoption model, where cognitive trust measured in three sub-dimensions (trust in competence, trust in benevolence and trust in integrity) influences affective trust and later purchase intention. Experience with opinions usage and trust-based adoption model constructs are explained by some of consumer decision-making styles measured in ten dimensions (it was assumed that only selected styles will be useful). number 3 • fall 2016 Radoslaw Majcik Because of exploratory character of the study three main research questions have been formulated: rqi How previous consumer experience with product comparison site usage and opinions about products and sellers usage are connected with trust toward product comparison site constructs from trust-based adoption model? rq2 Which and how consumer decision-making styles are influencing the level of experience with product comparison site usage and opinions about products and sellers? rq3 Which and how consumer decision-making styles are influencing constructs from trust-based adoption model for product comparison site usage? No exact hypotheses were assumed for this research, particularly the set of consumer decision-making styles included in model was exactly exploratory, and modified during the modelling. Research model derived from conceptual one has been assessed via structural equation modelling approach utilizing pls-sem - recommended for exploratory stages of theory extensions (Hair, Ringle, and Sarstedt 2011) - and later via multi-group analysis using pls-mga algorithms. Sample and Measures sample Data have been collected during March 2015 through cawi questionnaire with e-mail invitation sent to authors students and their peers, that returned 461 usable responses from 575 sent invitations, giving response rate of 80.2%. Students were awarded small increase in course activity grade for participation and recruitment of their peers (this award was less than 4% of total possible grade). In effect, the sample consists of 60.2% women and 39.8% men. The average age of participants is 24.5 years with standard deviation of 5.1 years (range: 18-46 years old, median: 23 years). Each 1/3rd of participants were inhabitants of different level of urbanization areas: rural areas, small towns and larger cities. All participants must be active internet users and make at least one online purchase during a year prior study. Sample structure regarding gender and age is close to population of both full-time and part-time students of public university located in the South-Eastern part of Poland, where the data have been collected. measures Items to measure constructs used in this study were adapted from previously published research or have been developed by the au- 220 management • volume 11 Consumer Decision-Making Styles table 2 Scales Used in Study Construct (1) (2) (3) (4) Consumer experience in product comparison sites usage Consumer Experience Own developmenta N/A 9 Cognitive Trust in Competence ct_Competence McKnight, Choudhury, and Kacmar (2002) travestation 4 (3)b Cognitive Trust in Benevolence ct_Benevolence McKnight, Choudhury, and Kacmar (2002) travestation 4 (3)b Cognitive Trust in Integrity ct_Integrity McKnight, Choudhury, and Kacmar (2002) travestation 4 (3)b Affective Trust Affective Trust Komiak and Ben-basat (2006) reconstruction 4 Purchase Intention Purchase Intention Gefen, Kara-hanna and Straub (2003) reconstruction 4 Product Reviews Usage Product Reviews Usage Own developmenta N/A 2 Sellers Reviews Usage Sellers Reviews Usage Own developmenta N/A 2 Brand Conscious Style bc Sproles and Kendall (1986) reconstruction 3 Confused by Overchoice Style co Sproles and Kendall (1986) reconstruction 3 Ecologically Aware Style eco Own developmentc N/A 3 Perfectionistic Style perf Sproles and Kendall (1986) reconstruction 3 Continued on the next page thor. As questionnaire language was Polish, this required to translate and culturally adapt (by authors) scales written originally in English, including reconstruction procedures where needed. In effect, used scales are derived from original measures. Basic data about used scales is provided in table 2. Data analysis for this study has been performed using Smartpls 3.2 software (see www.smartpls.com), as most of the measurement variables were not normally distributed. Bootstrap procedure (resampling with replacement, sample size equal of original sample size - 461 observations) with 10000 repetitions for pls procedure and 5000 repetitions for pls-mga algorithm has been utilised to get inference statistics for measures and evaluated models. number 3 • fall 2016 Radoslaw Majcik table 2 Continued from the previous page Construct (1) (2) (3) (4) Price-Value Conscious Style pvc Sproles and Kendall (1986) reconstruction 3 Recreational Shopping Conscious Style rsc Sproles and Kendall (1986) reconstruction 3 notes Column headings are as follows: (1) name in tables and diagrams, (2) items derived from, (3) level of adaptation, (4) number of items. Only consumer decisionmaking styles included in model are shown in table, other four excluded. a Used also in Macik and Macik (2016b). b One item dropped due to low factor loading. cUsed also inMacik and Macik (2016a). Only consumer decision-making styles included in model are shown in table, other four excluded. reliability and validity of measures Reliability of measures in this study has been assessed by two commonly used measures: Cronbach's Alpha coefficient and Composite Reliability (cr) measure, as they represent lower and upper boundaries of true scale reliability respectively (Henseler, Ringle, and Sarstedt 2015). Using both criterions reliability of most constructs meets typical requirements - values of crs are all over suggested value 0.7 (Hair, Ringle, and Sarstedt 2013, 7), with some Alphas for co, perf and pvc lower than desired - tables 3 and 4. table 3 Reliability of Measures: Cronbach's Alpha Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust 0.802 0.801 0.020 39.344 0.000 0.758 0.837 bc 0.719 0.718 0.024 29.446 0.000 0.667 0.762 co 0.618 0.616 0.035 17.797 0.000 0.542 0.679 ct in Benevolence 0.713 0.710 0.029 24.506 0.000 0.649 0.763 ct in Competence 0.732 0.730 0.027 27.218 0.000 0.675 0.779 ct in Integrity 0.777 0.775 0.023 33.363 0.000 0.726 0.817 Consumer Experience 0.928 0.928 0.006 157.430 0.000 0.916 0.938 eco 0.788 0.788 0.020 39.697 0.000 0.746 0.824 perf 0.566 0.565 0.031 18.258 0.000 0.501 0.623 pvc 0.617 0.615 0.036 17.283 0.000 0.541 0.680 Product Reviews Usage 0.788 0.788 0.025 31.112 0.000 0.735 0.834 Purchase Intention 0.797 0.796 0.021 37.739 0.000 0.750 0.833 rsc 0.867 0.866 0.012 74.794 0.000 0.841 0.888 Sellers Reviews Usage 0.835 0.834 0.021 40.181 0.000 0.791 0.872 notes (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) i-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. 632 management • volume 11 Consumer Decision-Making Styles table 4 Reliability of Measures: Composite Reliability (cr) Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust 0.871 0.870 0.012 75.399 0.000 0.846 0.891 bc 0.840 0.829 0.035 23.985 0.000 0.662 0.852 co 0.787 0.772 0.047 16.631 0.000 0.569 0.810 ct in Benevolence 0.839 0.838 0.014 61.789 0.000 0.810 0.863 ct in Competence 0.849 0.848 0.013 66.398 0.000 0.822 0.872 ct in Integrity 0.871 0.870 0.012 74.503 0.000 0.845 0.891 Consumer Experience 0.940 0.940 0.005 198.685 0.000 0.930 0.948 eco 0.870 0.848 0.083 10.531 0.000 0.263 0.885 perf 0.770 0.762 0.024 31.480 0.000 0.678 0.789 pvc 0.794 0.789 0.019 42.068 0.000 0.736 0.816 Product Reviews Usage 0.904 0.904 0.010 87.166 0.000 0.884 0.924 Purchase Intention 0.868 0.867 0.012 72.181 0.000 0.841 0.889 rsc 0.915 0.909 0.036 25.182 0.000 0.850 0.926 Sellers Reviews Usage 0.924 0.923 0.009 104.208 0.000 0.906 0.940 notes (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) i-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. table 5 Convergent Validity of Measures: Average Variance Extracted (ave) Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust 0.628 0.627 0.024 26.461 0.000 0.580 0.672 bc 0.637 0.624 0.036 17.838 0.000 0.444 0.658 co 0.558 0.546 0.038 14.834 0.000 0.390 0.590 ct in Benevolence 0.635 0.634 0.023 27.447 0.000 0.587 0.678 ct in Competence 0.652 0.651 0.022 29.196 0.000 0.607 0.694 ct in Integrity 0.692 0.691 0.022 31.513 0.000 0.645 0.731 Consumer Experience 0.636 0.636 0.019 33.588 0.000 0.598 0.672 eco 0.691 0.667 0.072 9.558 0.000 0.238 0.721 perf 0.534 0.528 0.022 24.125 0.000 0.464 0.558 pvc 0.564 0.560 0.026 22.127 0.000 0.497 0.601 Product Reviews Usage 0.825 0.825 0.017 47.873 0.000 0.792 0.859 Purchase Intention 0.622 0.621 0.024 25.756 0.000 0.572 0.667 rsc 0.782 0.772 0.042 18.623 0.000 0.646 0.807 Sellers Reviews Usage 0.858 0.858 0.015 56.201 0.000 0.827 0.887 notes (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) i-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. Convergent validity for used measures assessed via Average Variance Extracted (ave) is very good - all constructs are meeting the cri- number 3 • fall 2016 Radoslaw Majcik table 6 Discriminant Validity of Measures: Fornell Larcker Criterion Const. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (n) (12) (13) (14) (1) 0.792 (2) 0.171 0.798 (3) -0.039 0.150 0.747 (4) 0.609 0.096 -0.026 0.797 (5) 0.747 0.161 -0.022 0.657 0.807 (6) 0.633 0.099 -0.035 0.691 0.700 0.832 (7) 0.246 0.086 0.067 0.100 0.205 0.167 0.798 (8) 0.132 -0.013 0.081 0.047 0.069 0.019 0.210 0.831 (9) 0.070 0.310 -0.020 -0.018 0.111 0.014 0.220 0.125 0.731 (10) 0.188 0.181 0.156 0.152 0.232 0.175 0.162 0.199 0.233 0.751 (11) 0.262 0.110 0.173 0.121 0.185 0.121 0.327 0.145 0.142 0.074 0.908 (12) 0.739 0.191 0.022 0.500 0.597 0.504 0.285 0.148 0.039 0.209 0.223 0.788 (13) 0.188 0.171 0.073 0.143 0.162 0.148 0.040 0.090 0.069 0.336 0.053 0.198 0.884 (14) 0.228 0.143 0.033 0.202 0.209 0.140 0.327 0.057 0.169 0.072 0.571 0.211 0.013 0.926 notes Constructs: (1) Affective Trust, (2) bc, (3) co, (4) ct in Benevolence, (5) ct in Competence, (6) ct in Integrity, (7) Consumer Experience, (8) eco, (9) perf, (10) pvc, (11) Product Reviews Usage, (12) Purchase Intention, (13) rsc, (14) Sellers Reviews Usage. Numbers on matrix diagonal are square roots from ave for constructs; numbers off-diagonal are correlations between them, this is alternative form to report Fornell-Larcker Criterion (Henseler, Ringle, and Sarstedt 2014, 117). terion of ave value higher than 0.5 as suggested by Fornell and Larcker (1981) - table 5. Even for constructs having lower internal consistency in terms of Cronbach's Alpha (co, perf and pvc) the ave values are at least satisfactory. Discriminant validity of used measures is also at very good (table 6). The Fornell-Larcker Criterion stating that ave for each construct should be higher from all squared correlations between particular construct and other measures (Fornell and Larcker 1981) is met for all constructs (see also note for table 6, as in mentioned table this criterion is reported in alternative form). Results whole sample model On the base or conceptual model shown on figure 2 and initial data analysis structural equations model presented on figure 3 has been estimated using SmartPLS 3.2 software. Previous analysis (Ma-cik and Mapk 2016) confirmed the validity of trust-based adoption model to explain purchase intention in product comparison site environment. In structural model depicted on figure 3 consumer experience explains both reviews constructs, that are also interconnected - as in virtual channel product choice is typically made earlier than vendor/seller choice, so it was assumed that product review usage should explain sellers review usage, also because of similar factors influencing reviews following as a whole - persons more often using 634 management • volume 11 Consumer Decision-Making Styles figure 3 Research Model with Results Obtained via pls-sem (values on paths are standardized path coefficients with bootstrap obtained p-values reported in parentheses) product reviews inside product comparison engine are more likely more heavily relying on sellers reviews, to establish sellers credibility. Estimated model exhibits reasonable fit - proportion of variance explained, measured with R2 statistics is over 0.5 for main explained variables, particularly 0.612 for Affective Trust and 0.551 for Purchase Intention. The level of coefficients of determination (R2) for all constructs playing roles of dependent variables are presented in table 7. Also low srmr (Square Root of Mean Residuals) value on the level of 0.039 suggests reasonable model fit to the data. Table 8 presents in detail path coefficient values in original sample and inference statistics for paths obtained via bootstrapping. Path coef-ficientsfrom original sample with significance levels are also shown on figure 3. In general, consumer experience with product and sellers reviews number 3 • fall 2016 Radoslaw Majcik table 7 Coefficients of Determination for Dependent Variables in Estimated Model (R2 Values) Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust 0.612 0.617 0.034 17.974 0.000 0.559 0.691 ct in Benevolence 0.436 0.438 0.046 9.528 0.000 0.352 0.530 ct in Competence 0.106 0.119 0.029 3.676 0.000 0.086 0.207 ct in Integrity 0.482 0.483 0.044 11.005 0.000 0.401 0.571 Consumer Experience 0.061 0.071 0.025 2.462 0.014 0.038 0.143 Product Reviews Usage 0.130 0.137 0.030 4.267 0.000 0.090 0.215 Purchase Intention 0.551 0.555 0.041 13.311 0.000 0.482 0.640 Sellers Reviews Usage 0.348 0.351 0.040 8.603 0.000 0.279 0.436 notes (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) i-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. table 8 Path Coefficients in Estimated Model Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust ^ Purchase Intention 0.726 0.726 0.032 22.941 0.000 0.663 0.786 bc ct in Competence 0.088 0.097 0.046 1.916 0.055 0.024 0.206 co ^ Product Reviews Usage 0.152 0.159 0.043 3.529 0.000 0.089 0.256 ct in Benevolence ^ Affective Trust 0.146 0.146 0.051 2.866 0.004 0.045 0.245 ct in Benevolence ^ ct in Integrity 0.680 0.678 0.033 20.335 0.000 0.607 0.737 ct in Competence ^ Affective Trust 0.515 0.513 0.049 10.578 0.000 0.411 0.603 ct in Competence ^ ct in Benevolence 0.643 0.642 0.038 16.957 0.000 0.567 0.714 ct in Integrity ^ Affective Trust 0.156 0.157 0.049 3.208 0.001 0.064 0.257 Consumer Experience ^ Product Reviews Usage 0.317 0.317 0.042 7.529 0.000 0.235 0.402 Consumer Experience ^ Sellers Reviews Usage 0.157 0.158 0.044 3.600 0.000 0.074 0.245 eco ^ Affective Trust 0.069 0.072 0.033 2.073 0.038 0.015 0.142 perf ^ Consumer Experience 0.193 0.202 0.050 3.849 0.000 0.120 0.313 pvc ^ ct in Competence 0.174 0.176 0.056 3.107 0.002 0.071 0.288 pvc ^ ct in Integrity 0.072 0.074 0.037 1.967 0.049 0.005 0.149 pvc ^ Consumer Experience 0.117 0.119 0.053 2.222 0.026 0.020 0.227 pvc ^ Purchase Intention 0.072 0.074 0.033 2.218 0.027 0.015 0.143 Product Reviews Usage ^ Affective Trust 0.121 0.121 0.033 3.678 0.000 0.057 0.186 Product Reviews Usage ^ Sellers Reviews Usage 0.519 0.519 0.039 13.342 0.000 0.442 0.594 rsc ^ ct in Competence 0.086 0.091 0.041 2.091 0.037 0.020 0.181 Sellers Reviews Usage ^ ct in Benevolence 0.068 0.068 0.036 1.907 0.057 -0.003 0.136 Sellers Reviews Usage ^ ct in Competence 0.182 0.181 0.044 4.122 0.000 0.090 0.265 notes (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) i-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. usage are loosely connected with trust-based adoption model constructs. Also the direct influence of six selected (on the base of correlation analysis) consumer decision-making styles is not so strong, although those relationships are statistically significant. Magnitude of consumer decision-making styles influence increases when total effects (including indirect effects) are taken into account. As the model is quite complicated, some indirect effects are pres- 636 management • volume 11 Consumer Decision-Making Styles table 9 Total Effects in Estimated Model Constructs (1) (2) (3) (4) (5) (6) (7) Affective Trust — Purchase Intention 0.726 0.726 0.032 22.941 0.000 0.663 0.786 *bc — Affective Trust 0.060 0.066 0.032 1.872 0.061 0.015 0.143 *bc — ct in Benevolence 0.057 0.062 0.030 1.895 0.058 0.014 0.134 bc — ct in Competence 0.088 0.097 0.046 1.916 0.055 0.024 0.206 *bc — ct in Integrity 0.039 0.042 0.021 1.852 0.064 0.009 0.093 *bc — Purchase Intention 0.043 0.048 0.024 1.825 0.068 0.010 0.106 *co — Affective Trust 0.030 0.031 0.010 2.960 0.003 0.014 0.053 *co — ct in Benevolence 0.015 0.015 0.005 2.669 0.008 0.006 0.027 *co — ct in Competence 0.014 0.015 0.005 2.629 0.009 0.006 0.027 *co — ct in Integrity 0.010 0.010 0.004 2.679 0.007 0.004 0.018 co — Product Reviews Usage 0.152 0.159 0.043 3.529 0.000 0.089 0.256 *co — Purchase Intention 0.021 0.022 0.007 2.994 0.003 0.010 0.038 *co — Sellers Reviews Usage 0.079 0.083 0.023 3.465 0.001 0.045 0.134 ct in Benevolence — Affective Trust 0.252 0.252 0.046 5.496 0.000 0.164 0.343 ct in Benevolence — ct in Integrity 0.680 0.678 0.033 20.335 0.000 0.607 0.737 *ct in Benevolence — Purchase Intention 0.183 0.183 0.035 5.286 0.000 0.117 0.252 ct in Competence — Affective Trust 0.676 0.675 0.035 19.395 0.000 0.601 0.739 ct in Competence — ct in Benevolence 0.643 0.642 0.038 16.957 0.000 0.567 0.714 *ct in Competence — ct in Integrity 0.437 0.436 0.042 10.424 0.000 0.352 0.516 *ct in Competence — Purchase Intention 0.491 0.490 0.039 12.588 0.000 0.412 0.564 ct in Integrity — Affective Trust 0.156 0.157 0.049 3.208 0.001 0.064 0.257 *ct in Integrity — Purchase Intention 0.113 0.114 0.035 3.209 0.001 0.046 0.184 *Consumer Experience — Affective Trust 0.084 0.083 0.018 4.678 0.000 0.050 0.119 *Consumer Experience — ct in Benevolence 0.060 0.059 0.017 3.510 0.000 0.027 0.092 *Consumer Experience — ct in Competence 0.059 0.059 0.017 3.396 0.001 0.025 0.093 *Consumer Experience — ct in Integrity 0.041 0.040 0.012 3.501 0.000 0.018 0.062 Consumer Experience — Product Reviews Usage 0.317 0.317 0.042 7.529 0.000 0.235 0.402 *Consumer Experience — Purchase Intention 0.061 0.061 0.013 4.608 0.000 0.036 0.086 Consumer Experience — Sellers Reviews Usage 0.322 0.323 0.045 7.180 0.000 0.236 0.409 eco — Affective Trust 0.069 0.072 0.033 2.073 0.038 0.015 0.142 *eco — Purchase Intention 0.050 0.052 0.024 2.076 0.038 0.010 0.103 *perf — Affective Trust 0.016 0.017 0.005 2.998 0.003 0.008 0.029 *perf — ct in Benevolence 0.012 0.012 0.005 2.500 0.012 0.004 0.022 *perf — ct in Competence 0.011 0.012 0.005 2.429 0.015 0.004 0.022 *perf — ct in Integrity 0.008 0.008 0.003 2.506 0.012 0.003 0.015 perf — Consumer Experience 0.193 0.202 0.050 3.849 0.000 0.120 0.313 *perf — Product Reviews Usage 0.061 0.064 0.018 3.371 0.001 0.034 0.106 *perf — Purchase Intention 0.012 0.012 0.004 2.965 0.003 0.006 0.021 *perf — Sellers Reviews Usage 0.062 0.066 0.020 3.094 0.002 0.034 0.113 *pvc — Affective Trust 0.139 0.140 0.041 3.345 0.001 0.063 0.225 *pvc — ct in Benevolence 0.119 0.120 0.037 3.226 0.001 0.052 0.195 pvc — ct in Competence 0.181 0.183 0.057 3.197 0.001 0.076 0.296 Continued on the next page ent. As total effect is the sum of direct effect and indirect effect(s), only direct and total effects are reported (tables 8 and 9). The indirect effect, in this case, is easy to calculate as the difference between total and direct effects (or as multiplication of particular path coefficients). In the case of lack of direct relationship total effect equals indirect effect - such cases are marked with asterisk table 9. number 3 • fall 2016 227 Radoslaw Majcik table 9 Continued from the previous page *pvc — ct in Integrity 0.153 0.155 0.052 2.939 0.003 0.057 0.262 pvc — Consumer Experience 0.117 0.119 0.053 2.222 0.026 0.020 0.227 *pvc — Product Reviews Usage 0.037 0.038 0.017 2.122 0.034 0.005 0.074 pvc — Purchase Intention 0.173 0.176 0.044 3.942 0.000 0.097 0.269 *pvc — Sellers Reviews Usage 0.038 0.039 0.018 2.064 0.039 0.005 0.077 Product Reviews Usage — Affective Trust 0.194 0.193 0.036 5.334 0.000 0.122 0.263 *Product Reviews Usage — ct in Benevolence 0.096 0.095 0.024 3.957 0.000 0.047 0.142 *Product Reviews Usage — ct in Competence 0.095 0.094 0.025 3.829 0.000 0.045 0.143 *Product Reviews Usage — ct in Integrity 0.065 0.065 0.017 3.936 0.000 0.031 0.095 *Product Reviews Usage — Purchase Intention 0.141 0.140 0.027 5.301 0.000 0.088 0.191 Product Reviews Usage — Sellers Reviews Usage 0.519 0.519 0.039 13.342 0.000 0.442 0.594 *rsc — Affective Trust 0.058 0.061 0.028 2.045 0.041 0.013 0.125 *rsc — ct in Benevolence 0.055 0.058 0.027 2.027 0.043 0.012 0.119 rsc — ct in Competence 0.086 0.091 0.041 2.091 0.037 0.020 0.181 *rsc — ct in Integrity 0.037 0.040 0.019 1.961 0.050 0.007 0.083 *rsc — Purchase Intention 0.042 0.045 0.021 1.989 0.047 0.009 0.093 *Sellers Reviews Usage — Affective Trust 0.140 0.139 0.031 4.540 0.000 0.075 0.197 Sellers Reviews Usage — ct in Benevolence 0.185 0.184 0.045 4.148 0.000 0.094 0.269 Sellers Reviews Usage — ct in Competence 0.182 0.181 0.044 4.122 0.000 0.090 0.265 *Sellers Reviews Usage — ct in Integrity 0.126 0.125 0.031 4.123 0.000 0.061 0.181 *Sellers Reviews Usage — Purchase Intention 0.102 0.101 0.023 4.409 0.000 0.053 0.144 notes Column headings are as follows: (1) original sample (o). Bootstrap estimates: (2) sample mean (M), standard error (sterr), (4) f-statistics (|o/sterr|), (5) p-values. Bootstrap bias corrected 95% confidence interval: (6) low, (7) up. * indirect effect only multi group comparisons regarding chosen brand In this study, consumers were expected to make choice of an automatic coffee machine (as a suggestion for a neighbour buy) in product comparison site environment. This choice has been recorded on the level of particular product recognizable by exact type (described as producer alphanumerical code). To form groups for comparison chosen brand has been used. Study participants can choose any of brands available in product comparison site although better-known brands (of large general table 10 Structure of Brand Choices Made by Research Participants with Size of Groups for pls-mga Groups of brands Brand name Group size (n) Share (%) Included for PLS-MGA analysis Saeco 150 32.5 De Longhi 107 23.2 Krups 75 16.3 Bosch 63 13.7 Siemens 42 9.1 Excluded from PLS-MGA analysis Severin 3 0.7 Zelmer 3 0.7 other 18 3-9 638 management • volume 11 table il Path Coefficients in Five Analysed Brand Groups Paths (direct effects) Coefficient estimates p-values (from bootstraping) (1) (2) (3) (4) (5) (1) (2) (3) (4) (5) Affective Trust ->■ Purchase Intention 0.707 0.744 0.639 0.773 0.638 0.000 0.000 0.000 0.000 0.000 bc ->■ ct in Competence 0.001 0.052 0.208 0.071 0.246 0.993 0.693 0.172 0.493 0.274 co Product Reviews Usage 0.318 -0.018 0.260 0.183 0.244 0.003 0.872 0.325 0.024 0.152 ct in Benevolence ->■ Affective Trust 0.197 0.304 -0.033 0.211 0.019 0.147 0.001 0.718 0.033 0.939 ct in Benevolence ->■ ct in Integrity 0.680 0.644 0.523 0.722 0.818 0.000 0.000 0.000 0.000 0.000 ct in Competence ->■ Affective Trust 0.511 0.390 0.579 0.562 0.385 0.000 0.000 0.000 0.000 0.023 ct in Competence ->■ ct in Benevolence 0.764 0.565 0.473 0.696 0.614 0.000 0.000 0.000 0.000 0.000 ct in Integrity Affective Trust 0.179 0.102 0.295 0.020 0.359 0.179 0.265 0.007 0.816 0.111 Consumer Experience ->■ Product Reviews Usage 0.114 0.372 0.286 0.398 0.030 0.420 0.000 0.023 0.000 0.878 Consumer Experience ->■ Sellers Reviews Usage 0.229 0.235 0.115 0.199 -0.131 0.043 0.006 0.353 0.009 0.435 eco —* Affective Trust 0.048 0.055 0.093 0.145 0.046 0.567 0.539 0.397 0.010 0.776 peef ->■ Consumer Experience 0.314 0.311 0.073 0.163 0.020 0.002 0.000 0.784 0.085 0.943 pvc ct in Competence 0.411 0.121 0.287 0.124 -0.029 0.004 0.448 0.030 0.325 0.850 pvc ->■ ct in Integrity 0.223 0.000 0.089 0.041 0.199 0.036 0.998 0.395 0.602 0.061 pvc ->■ Consumer Experience 0.260 0.155 0.082 0.037 0.360 0.065 0.226 0.657 0.773 0.037 pvc ->■ Purchase Intention 0.078 0.059 0.261 -0.038 0.150 0.366 0.410 0.003 0.646 0.412 Product Reviews Usage ->■ Affective Trust 0.195 0.215 -0.032 0.079 0.325 0.016 0.001 0.682 0.149 0.002 Product Reviews Usage ->■ Sellers Reviews Usage 0.543 0.411 0.555 0.527 0.538 0.000 0.000 0.000 0.000 0.000 esc ->■ ct in Competence -0.033 0.167 0.120 0.141 -0.212 0.845 0.089 0.366 0.095 0.413 Sellers Reviews Usage ->■ ct in Benevolence 0.006 0.083 0.169 0.029 0.146 0.949 0.289 0.087 0.621 0.303 Sellers Reviews Usage ->■ ct in Competence 0.298 0.222 -0.074 0.305 -0.199 0.006 0.011 0.495 0.000 0.282 notes (1) Bosch, (2) DeLonghi, (3) Krups, (4) Saeco, (5) Siemens. table 12 Significance of Differences Between Groups: pls-mga Non-Parametric Test p-Values Paths (direct effects) Significance of diff. between path coeff. in groups: p-values (from pls-mga test) (1)"(2) (i)"(3) (i)"(4) (i)"(5) (2)-(3) (2)-(4) (2)-(5) (3)-(4) (3)-(5) (4)"(5) Affective Trust ->■ Purchase Intention 0.621 0.281 0.714 0.331 0.102 0.649 0.196 0.944 0.525 0.132 bc ->■ ct in Competence 0.604 0.857 0.664 0.853 0.819 0.544 0.830 0.169 0.618 0.836 co Product Reviews Usage 0.022* 0.544 0.131 0.356 0.779 0.914 0.909 0.292 0.382 0.708 ct in Benevolence ->■ Affective Trust O.752 0.072 0.549 0.257 0.006* 0.244 0.141 0.965 0.567 0.227 ct in Benevolence ->■ ct in Integrity O.349 0.103 0.674 0.915 0.152 0.827 0.967 0.963 0.993 0.864 ct in Competence ->■ Affective Trust 0.227 0.657 0.614 0.283 0.912 0.910 0.522 0.434 0.150 0.168 ct in Competence ->■ ct in Benevolence O.O47* 0.022 0.250 0.174 0.244 0.909 0.658 0.963 0.798 0.318 ct in Integrity ->■ Affective Trust O.312 0.748 0.157 0.769 0.919 0.253 0.858 0.020* 0.631 0.913 Consumer Experience ->■ Product Reviews Usage O.938 0.820 0.964 0.359 0.288 0.596 0.058** 0.788 0.135 0.041* Consumer Experience ->■ Sellers Reviews Usage 0.508 0.248 0.401 0.047 0.215 0.373 0.035* 0.712 0.124 0.048* eco —* Affective Trust O.54I 0.635 0.831 0.480 0.610 0.818 0.458 0.645 0.388 0.263 peef ->■ Consumer Experience O.485 0.220 0.126 0.180 0.219 0.105 0.178 0.527 0.438 0.356 pvc ->■ ct in Competence O.O79** 0.262 0.056** 0.018* 0.799 0.502 0.241 0.168 0.063** 0.213 pvc ->■ ct in Integrity 0.067** 0.183 0.084** 0.441 0.732 0.624 0.909 0.346 0.773 0.886 pvc ->■ Consumer Experience O.29O 0.225 0.125 0.705 0.380 0.256 0.852 0.412 0.874 0.928 pvc ->■ Purchase Intention O.43O 0.933 0.165 0.644 0.960 0.185 0.684 0.012* 0.291 0.830 Product Reviews Usage ->■ Affective Trust 0.580 0.022* 0.115 0.840 0.009* 0.055** 0.820 0.877 0.996 0.982 Product Reviews Usage ->■ Sellers Reviews Usage O.I44 0.540 0.442 0.510 0.875 0.838 0.800 0.402 0.478 0.557 esc ct in Competence O.856 0.777 0.832 0.293 0.382 0.405 0.100** 0.547 0.147 0.118 Sellers Reviews Usage ->■ ct in Benevolence O.734 0.881 0.580 0.793 0.754 0.287 0.648 0.111 0.441 0.779 Sellers Reviews Usage ->■ ct in Competence O.284 0.010* 0.507 0.018* 0.017* 0.769 0.030* 0.998 0.268 0.012* notes Groups numbered as in table 11. * p <0.05, **0.05