[ieee 2010 43rd hawaii international conference on system sciences - honolulu, hawaii, usa...

Online Social Shopping: The Functions and Symbols of Design Artifacts

Camille Grange University of British Columbia [email protected]

Izak Benbasat

University of British Columbia [email protected]

Abstract We report the results from a study investigating

online shoppers’ perceptions and evaluations of online social shopping design artifacts. To do so, we use the framework developed by Markus and Silver (2008) for studying information technology artifacts and their effects. Hence, we examine the functional affordances, i.e., the potential uses, and the symbolic expressions, i.e., the underlying message guiding use, of a set of four artifacts that shopper-generated content :1) lists of (favorite) products from shoppers, 2) lists of online shopping friends, 3) product reviews, and 4) shoppers’ profiles. The results from a survey of online shopper are preliminary but promising. They show that recreational and non-recreational shoppers perceive different potential uses and underlying messages in the artifacts, and that the nature of these perceptions (utilitarian vs. hedonic) are related to shoppers’ assessment of the artifacts’ utilitarian and hedonic value. 1. Introduction

Prior research has shown that both utilitarian and hedonic aspects of the shopping experience were important contributors to the overall shopping value perceived by shoppers [2]. Despite the evidence that the two primary motivations for retail shopping (i.e., goal-oriented/ utilitarian and hedonic/ fun) also apply to the online environment [25], to date, e-commerce research has mostly focused on generating design guidelines targeted at utilitarian purposes (e.g., the study of the relationship between product presentation format and product understanding, [13]). Some of the reasons for this focus might include the fact that utilitarian shoppers are considered more attractive and profitable spending targets than recreational shoppers and that overall, satisfying the hedonic needs of users has not been considered essential in the Management Information Systems (MIS) field. However, one fifth to one third of online consumers are not engaged in narrow, goal-focused behavior but rather look for fun in their buying experiences [25]. In addition, these consumers are known to be impulse buyers [14] and

less driven by price than other utilitarian shoppers, which makes them attractive targets for marketers. It has also been observed that recreational shoppers [3] are more brand-loyal, tend to give advice to other persons with respect to buying decisions, and are more likely to be influential through word-of-mouth [24], which altogether attest to their importance for e-retailers.

Thereby, e-commerce research reflects a somewhat incomplete vision of user needs which constrains the potential of information technologies to help create online shopping experiences that are in line with the observed set of diversified shoppers’ motivations (e.g., diversion, self-gratification, learning about new trends, social experience, [22]). To help address this gap, the present paper proposes to study the design of online social shopping environments1. These environments may be materialized through general social shopping network websites (e.g., Kaboodle.com, yelp.com, storrz.com, stylehive.com) where online shoppers converge to share their varied product or service related interests, or through specific online retailers who create such community-like environments inside their own websites. In all cases, online social shopping environments are close to what Kozinetz [16] named, back in 1999, ‘virtual communities of consumption’, i.e., “specific subgroups of virtual communities that explicitly center upon consumption-related interests. They can be defined as affiliative groups whose online interactions are based upon shared enthusiasm for, and knowledge of, a specific consumption activity or related group of activities” [16, p.254].

Online social shopping represents an ideal context for studying the cohabitation of utilitarian and hedonic aspects of online shopping because it embeds characteristics that can support entertaining behaviors

1 To our knowledge, social shopping has not been defined and studied in research yet. Hence, we provide the definition from Wikipedia. “Social shopping is a method of e-commerce and of traditional shopping in which consumers shop in a social networking environment similar to MySpace. Using the wisdom of crowds, users communicate and aggregate information about products, prices, and deals. Many sites allow users to create custom shopping lists and share them with friends. Others concentrate on the user interactions consisting information and recommendations that are hard to acquire from the actual sales personnel. Some services even allow users to shop together synchronously to complete the social environment.”

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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010

978-0-7695-3869-3/10 $26.00 © 2010 IEEE

(e.g., the possibility to follow other shoppers’ activities and to explore new products) as well as goal-directed product search (e.g., the possibility to read product reviews in order to gain product knowledge and make better purchase decisions). In addition, these ‘social’ information technologies (IT) artifacts have interesting and unique capabilities. They may increase reach [19] by connecting a large number of shoppers, hence increasing the visibility of sellers’ positive and negative perceptions, and making it easier to gather the advice from a diversified set of people. Additionally, they may enhance representation [19] by enabling shoppers (i) to share their experiences with products and services, (ii) to get the community’s opinion on a product , (iii) to have access to up-to-date information and commentaries on products, (iv) to get information in an understandable (user-shopper) ‘language’, (v) to exploit the knowledge of expert shoppers, (vi), or (vii) to identify similar or attractive shoppers thanks to access to rich profiling information. Overall, online social shopping-related information technologies have the capability to readily provide online shoppers with rich and detailed resources that would be more difficult (sometimes impossible, e.g., how to get the overall opinion of buyers on a product) to gather in the real world. Hence, with the growing adoption of social technology [10], it is relevant and important to study the applicability and utility of IT artifacts leveraging user-generated content in the e-commerce realm. In addition, as the products most sold online (i.e., books, apparel, and leisure travel) [11] can be considered experience goods [15], we can expect that social shopping is well-positioned to bring additional value to online shoppers now and in the near future.

In that context, the aim of this paper is to investigate some online social shopping artifacts in terms of both the hedonic and the utilitarian value they may provide to their users. To investigate in deeper detail the perceptions and assessments of such artifacts, we examine them in terms of their potential for goal-oriented action as well as in terms of their general intent with respect to human values and symbols [17]. Note that this paper’s objective is not to evaluate the different artifacts: we test the propositions developed in section 3 with four different artifacts in order to cover each cell of the online social shopping artifacts classification provided in Table 1 (i.e., for external validity purpose) rather than to compare them

While many studies have focused on knowledge contribution to communities or networks, conversely few have attempted to examine the exploitation of knowledge from communities or networks, and especially not in the domain of online shopping. Hence, the present paper aims to contribute in terms of both a better understanding of the application of digital

social media to e-commerce, and a first attempt (to our knowledge) to apply Markus and Silver’s foundational framework to the study of IT use and effects [17].

In the remainder of this paper, we first discuss the nature of online social shopping artifacts, and describe the theoretical groundings and assumptions we build on to study their usage. We then develop research propositions followed by the details from an empirical study of online social shopping artifacts and the preliminary insights they provide.

2. The use and value of online social shopping design artifacts 2.1. The design artifacts of online social shopping

Our analysis focuses on individual online shoppers extracting value from a network of people and product related resources. Among the various types of resources that may be provided in online shopping environments, first there are resources about the products, that are created by some institutions (i.e., the retailer or the product owner), e.g., product specifications, pictures, demos, price. Second, there are the resources produced by other shoppers and which pertain to the products (e.g., product reviews, ratings, scores, lists of favorite products, lists of favorite brands). Third, there are resources produced by online shoppers, that pertain to shoppers themselves and their relationships with others (e.g., shoppers’ interests and hobbies, their friendship networks, their belonging to certain shopping-related communities). Each type of resource can provide value to online shoppers; e.g., ‘search’ goods may be easily evaluated based on institution-provided product information, while ‘experience’ goods may gain by having shoppers provide insights about the product in use [15]. Moreover, the personal and interpersonal information (i.e., people-related information) provided by shoppers may help identify individuals that are influential, attractive, or similar to a consumer so that they can exchange information on their favorite products.

In the context of the present study, we call online social shopping design artifacts the E-commerce-related artifacts that leverage the participation of other shoppers, i.e., the artifacts that exploit people-related as well as product-related user-generated content. Drawing from Borgatti and Cross’s work on advice seeking in social networks [5], we categorize artifacts according to how they can help people extract value from a social network, i.e., according to whether they help shoppers (1) be aware and access the informational resources of the network of shoppers

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(these resources may be about products or shoppers), or (2) evaluate these product or people-related informational resources. Hence, online social shopping artifacts can be classified according to a two-dimensional framework, which is presented in Table 1 along with four illustrative artifacts. The first artifact, product reviews (A1), enables the evaluation of certain products throughout the eyes of other shoppers. Second, online shoppers’ profiles (A2) enable shoppers to evaluate who a shopper is (i.e., evaluate people-related informational resource). Third, shoppers-generated product lists (A3), sometimes also called shoppers wish lists, provide shoppers with a direct access to products. Fourth, list of online shopping friends (A4) provide direct access to other shoppers’ profiles (i.e., access to people-related informational resource such as who the shopper is and what are his or her interests) as well as, ultimately, access to the product-related informational resources created by these shoppers (i.e., indirect access to shopper-generated product information such as whether the shopper would recommend the product, and what he or she thinks the pros and cons are). While we acknowledge that there could be other ways of classifying social online shopping design artifacts, our purpose at this stage has been to propose a classification which (1) is specific to the e-commerce realm (hence the product vs. people dimension), and (2) which builds on the activities of social network exploitation (hence the awareness/access vs. evaluation dimension).

Table 1 – Online social shopping artifacts

The artifact provides informational

resources that pertain to... The artifact enables resource…

Product

People

Evaluation Product review (A1)

Online shopper profile (A2)

Awareness/ Access

Shopper-created products list (A3)

List of online shopping friends (A4)

2.2. A framework for studying IT design artifacts

To help describe the social shopping design artifacts and examine their usage and potential effects, we utilize a framework from Markus and Silver [17] which enables a detailed look into human-technology interaction and its potential consequences. This work is rooted in, and advances, the notions of “structural features” and “spirit” originally suggested by

DeSanctis and Poole in the Adaptive Structuration Theory (AST) [9].

The study of IT artifacts has been problematic in the MIS literature inasmuch as we lack an agreed-upon theoretical framework to study them ([18, 4]). AST [9] is an insightful attempt to examine the usage and the impacts of advanced information technologies (i.e., these technologies that do more than just support transactions, by enabling coordination and social interactions for example), such as group decision support systems, in organizations. An important contribution of this work was the development of the two key concepts of structural features (the rules, resources, and capabilities of an IT) and spirit (the general intent with regard to values and goals underlying a given set of structural features) as alternatives to the traditional conceptualizations of IT artifacts in terms of designers’ intentions and user perceptions. DeSanctis and Poole recognized that users’ perceptions of the spirit of an IT artifact might overlook certain aspects, and that in the meantime, designers’ intentions might not be fully realized in an artifact. For example, this problem could be due to disagreements and constraints on design decisions. It was also acknowledged that different stakeholders (e.g., designers and users) might not similarly perceive an IT artifact’s spirit, making it incoherent in a sense (such as, when a designer aims at designing a feature that supports democratic participation and users perceive the feature as authoritarian and dominant). Additionally, it was highlighted that an IT artifact may be appropriated unfaithfully, i.e., that structural features may be perceived and used in a way that is not in line with the general intent of an IT in terms of its underlying values and goals. Overall, DeSanctis and Poole were pioneers in suggesting that an IT should be analyzed by considering human values, i.e., the spirit of an IT artifact. Markus and Silver [17] provide a more exhaustive review of DeSanctis and Poole ’s concepts of structural features and spirit as they discuss the contributions DeSanctis and Poole have made as well as the issues they have raised.

Building on AST, Markus and Silver developed a “foundation for the study of IT effects” by distinguishing between the IT artifacts (and their properties) - or technical objects, as they call them - and their relationship with end-users through the two channels of functional affordances (from the concept of affordance in ecological psychology) and symbolic expressions. In this novel conceptualization, technical objects are the IT artifacts (i.e., human-made) with their properties, functions, packaging, arrangement and appearance. Functional affordances refer to the potential uses one can make from a technical object, i.e., the possibilities for goal-directed behavior given

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one’s goals and capabilities. In turn, symbolic expressions refer to the underlying value-laden intent of a technical object that will guide use for a specific user group. A system might for example communicate democratic and freedom values (e.g., Wikipedia) or efficiency and control (e.g., ERP systems). Hence, they enhance prior work by providing two concepts that help distinguish but also bridge technical objects and the people who use them. Furthermore, they reposition the notion of spirit from being a property of a technology (which, as they mention, is ontologically troubling), to being the symbolic expressions potentially communicated by an IT to specific users. Examples of functional affordances and symbolic expressions are provided in Table 2 and 3 where we apply these concepts to an empirical study of online social shopping artifacts.

In addition to introducing three new concepts that help distinguish between the IT artifacts themselves and the relations between these artifacts and users, Markus and Silver [17] also urge researchers to carefully take into consideration users and their use environments. In fact, the ecological psychology paradigm from which they draw specifies that “to describe affordances (…) requires the researcher to specify the animal for which an object is an affordance” (p.619), or in other terms, that “researchers must consider interactions between actors and artifacts in light of the actor’s goals and capabilities” (p.620). As it is central to specify the user groups for which affordances and symbolic expressions are analyzed, we now describe the users’ motivations relevant in the online shopping context. 2.3. Shoppers’ motivations

The literature on shopper goals and motives is extremely rich. Different perspectives to the understanding of customers’ goals can be distinguished. First, some researchers have argued that shoppers can be classified into clusters exhibiting certain typical shared preferences, identities, and behavioral characteristics (e.g., [3, 6, 12]). Hence, many of them have worked on identifying shoppers’ typologies, arguing that such classifications would inform marketers to help better design retailing environments and choose the adequate techniques to satisfy, for instance, the whole spectrum of shoppers’ types. Other researchers have conceptualized consumers’ motives as a set of hierarchically organized cognitive schemes that may be activated in different circumstances (e.g., [7]). While we recognize that shopping motives can be studied under both a dispositional and a situational lens, we opt, at this stage of our research, for the dispositional perspective, i.e.,

for the stable shopper traits that are likely to influence perceptions of online social shopping design artifacts. Accordingly, shoppers can be differentiated according to some stable general tendencies towards shopping which express more or less experiential versus task-oriented motives. These two motives were defined as (i) shoppers’ drive for task-oriented, efficient, rational, and (ii) deliberate shopping, and shoppers’ desire to have fun, and be immersed in the store, respectively [25].

Recreational shoppers have been identified as those shoppers that enjoy shopping much or very much [3]. Recreational shoppers were shown to engage in more information-seeking, to make more unplanned purchases, and to enjoy social interactions. Conversely, economic shoppers’ were shown to position purchase as their main motive and to favor the minimization of time and effort expenditures. Following that work, it has been shown that the tendency to be a recreational shopper was associated among others with shoppers being more sociable, deal prone, and fashion-oriented [7]. More recently, an inventory of hedonic shopping motives was developed [1], containing the following six types: adventure (i.e., shopping for stimulation and for the feeling of being in another world), social (i.e., shopping with others), gratification (i.e., shopping for stress relief or to give a special treat to oneself), value (i.e., shopping for discounts and bargains), role (i.e., shopping for the excitement of finding the perfect gift for others), and idea related (i.e., shopping to keep up with trends and fashions). Overall, the body of work on shoppers’ goals attests to the variety of different motives people can have when shopping, but nonetheless largely agrees on the relevance of distinguishing between recreational / hedonic-oriented vs. efficiency / task-oriented shoppers. 3. Research propositions 3.1 Effects of artifacts’ functional affordances and symbolic expressions on behavioural beliefs

Perceived usefulness (PU) is an important behavioural belief shaping individuals’ acceptance of technologies [8]. While PU is utilitarian and extrinsic in nature, perceived enjoyment (PE) relates to the hedonic and intrinsic aspects of individuals motivations. In the IS adoption literature, PU has been considered foremost in task-efficiency contexts (such as work in organizations). Conversely, PE has been considered to have a stronger effect than PU on usage intentions for the case of hedonic IS (e.g., the usage of a movie website, [23]). Following prior work on the

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influence of websites’ task-relevant and mood-relevant cues on PU and PE [20], we expect for each social shopping design artifact, that utilitarian affordances and symbols will contribute more to an utilitarian belief such as PU, and that conversely, hedonic affordances and symbols will contribute more to forming a hedonic belief such as PE. Hence, we propose P3 to P6:

P1 – The higher the level of utilitarian functional affordances (FAu) perceived by online shoppers for a social shopping artifact, the higher the shopper’s evaluation of the artifact’s PU will be. In addition, this effect of FAu on PU will be stronger than the effect of FAh on PU.

P2 The higher the level of utilitarian symbolic expressions (SEu) perceived by online shoppers for a social shopping artifact, the higher the shopper’s evaluation of the artifact’s PU will be. Additionally, this effect of SEu on PU will be stronger than the effect of SEh on PU.

P3 – The higher the level of hedonic functional affordances (FAh), perceived by online shoppers for a social shopping artifact, the higher the shopper’s evaluation of the artifact’s PE will be. In addition, this effect of FAh on PE will be stronger than the effect of FAu on PE.

P4 - The higher the level of hedonic symbolic expressions (SEh), perceived by online shoppers for a social shopping artifact, the higher the shopper’s evaluation of the artifact’s PE will be. In addition, this effect of SEh on PE will be stronger than the effect of SEu on PE.

3.2 Online shoppers’ differences in utilitarian and hedonic FAs and SEs

While consensus on how to adopt and use a technology (consensus on appropriation, [21] may be an important prerequisite for group and collaboration systems, there is no clear evidence supporting that same assumption when it comes to individual shoppers using the same IT artifact without collaborating with each other. In online shopping for example, it seems reasonable to expect the contrary as designing an IT so that it can be adopted differently by audiences with different skills and motives is likely to be efficient given that these artifacts would be more flexible and adaptable to various users’ needs. As mentioned in this paper’s introduction, we suggest that online social

shopping artifacts have the potential to be perceived and used differently according to shoppers’ motives. Therefore, hedonic-oriented shoppers (i.e., recreational shoppers) should be more inclined to perceive hedonic functional affordances and symbols while non-recreational shoppers should be more inclined to perceive utilitarian affordances and symbols. Hence, we propose P5 and P6:

P5 – Recreational and non-recreational shoppers will be different in terms of their perception of the functional affordances and the symbolic expressions of online social shopping design artifacts.

P6 – There will be a fit between shopper type and the scores on utilitarian/hedonic FA/SE. Recreational shoppers will score higher on FAh and SEh, that is, they will perceive a higher degree of hedonic functional affordances and symbolic expressions than non-recreational shoppers. In contrast, non recreational shoppers will score higher on FAu and SEu than recreational shoppers.

4. Research design and data collection

The key constructs that have been involved in the research propositions are perceived usefulness (PU), perceived enjoyment (PE), hedonic and utilitarian functional affordances (FAh and FAu, respectively), hedonic and utilitarian symbolic expressions (SEh and SEu, respectively), and recreational vs. non recreational shopper type. We reuse existing work to develop measures for PU and PE [8, 23, 20]. Similarly, we adapt Bellenger and Korgaonkar’s measure of recreational shopper [3] which we reword for online shopping (vs. shopping alone). The FA and SE constructs from Markus and Silver have not been operationalized yet. Therefore, we first develop measures that would be appropriate for the online social shopping context and its coexisting utilitarian and hedonic aspects, i.e., FAh, FAu, SEh, and SEu. The following paragraphs detail the procedure that was followed to do so. 4.1. The measurement of functional affordances and symbolic expressions

In order to develop the instrument for measuring FAs and SEs, the three following stages were followed: item creation, scale development, and instrument testing. We carried out the “item creation” stage by reviewing the literature on the two-dimensional aspect

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of shopping experience and value. For the second stage, two card sorting rounds were carried out with panels of judges placing items in different categories. Finally, we tested scales’ reliabilities when conducting the analysis from the field study.

To create the items for utilitarian and hedonic symbolic expressions, we reviewed the literature examining the dual aspect of the shopping and online shopping experience (more or less utilitarian vs. hedonic). From that review, we extracted the following SEH: freedom, curiosity, choice, surprise, fun, creativity, fantasy, and the following SEU: informativeness, quick and simple, control, convenience, efficiency, immediacy, and rationality. Concerning the utilitarian and hedonic functional affordances (that we label FAU and FAH, respectively), we created a set of items that were representative FAs for the set of IT objects under study (e.g., for FAU: Get a convenient shortcut to products, find out reliable information on products; for FAH: find ideas for shopping, explore my friends’ favorite products). At this point, our aim was not to find out an exhaustive set of items measuring FAU and FAH but to identify items that were relevant for the set of IT artifacts under study, and that online shoppers would clearly categorize as hedonic/entertaining or as utilitarian.

In two separate card sorting exercises (one for the FAs, and another one for the SEs), and in two different rounds, we asked judges to sort the items identified as stated above, in predefined categories. 4.1.1. Symbolic expressions - First round. Eight judges (five PhD students in MIS, and three online shoppers) were asked to classify a set of randomized items into the following categories: (1) characteristics / symbols of a website that promote fun and playfulness, (2) characteristics / symbols of a website that promote task-completion and utilitarianism, and (3) don’t know. The overall hit ratio was 93%, with a total of 102 over 110 items located in the appropriate category. Each item was correctly categorized more than 75% of the time, except for the choice item which appeared to be ambiguous (three judges placed it in SEH, three in SEU and two in the “don’t know” category). 4.1.2. Symbolic expressions – Second round. The exercise was repeated with some modifications: we replaced choice by variety, and the panel of judges was different and contained less experts PhD students in MIS (over 10 judges, there was two MIS PhD students and eight regular online shoppers). In order to reduce the number of items, it was decided to split the “don’t know” category in three distinct ones: don’t know, both, none. By doing so, we allowed judges more leeway to categorize the items. The variety item still

performed worse than the other items (60% of appropriate placement). Removing this item and the three other ones that obtained less than 70% of appropriate placement (freedom, convenience, and immediacy) resulted in an overall hit ratio of 84% for the ten remaining items. The detailed placement for these ten final items is presented in Table 2.

Table 2. Items measuring symbolic expressions

Hedonic symbolic expressions (SEh)

Utilitarian symbolic expressions (SEu)

Surprise Quick and simple Curiosity Control Fun Efficiency Creativity Rationality Fantasy Informativeness

4.1.3. Functional affordances – First round. Nine judges (seven were MIS PhD students and two online shoppers) were asked to classify a set of randomized items (presented in Table 5) in the following categories: (1) A shopping website that promotes entertainment and playfulness helps me..., (2) A shopping website that promotes task-completion and utilitarianism helps me..., and (3) don’t know. Although the overall hit ratio for the whole set of 31 items reached 82%, it appeared that judges had difficulty categorizing hedonic FAs (hit ratio of 67% compared to 97% for FAU). For example, the item ‘get a convenient overview of who shoppers are’ which we developed as a FAU for the people-related design artifact ‘List of shoppers’ obtained a 22.2% good placement rate and 33% of the judges misplaced it in the entertainment category. As a consequence of the ambiguity of certain items, we reworded them in order to emphasize the utilitarian aspects, and ran another round of card sorting. 4.1.4. Functional affordances – Second round. Nine different judges participated in the second round of card sorting. We included fewer MIS PhD students in order to get the judgment of people more similar to regular online shoppers (over nine judges, there was one PhD student and eight online shoppers). This exercise resulted in an overall hit ratio of 83%, well-balanced between FAU (84%) and FAH (81%). After getting rid of the items that were still ambiguous (i.e., less than 70% of correct placement), we were left with the 26 remaining items for which the overall hit ratio reached 87% (86% and 88% for FAU and FAH, respectively), as can be seen in Table 3.

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Table 3. Items measuring functional affordances

Hedonic functional affordances (FAh)

Find ideas for shopping Explore products that are popular in the shopping community Explore my friends' favorite products Get informed about my friends' shopping activities Find out unexpected bargains Get to know what other shoppers have bought or liked Explore other shoppers' interests or hobbiesDiscover innovative products Follow-up shopping trends Affiliate / meet with others Explore new trends and unknown productsDiscover the variety of interests in the shopping community Communicate with other shoppers

Utilitarian functional affordances (FAu)

Get a convenient shortcut to products Get a quick access to products Find out reliable information on products Get a convenient access to products Learn more on products' characteristics Get recommendations to buy the best product(s) Weight a particular product's pros and cons Gather information on product characteristics Find products that will best suit my needs Engage in specific search Quickly narrow down the potential products I am interested in Get access to products that will fit my needs

4.2. Data collection

We created an online survey to investigate the propositions presented in section 3. Respondents were 138 Canadian and American e-commerce shoppers

recruited from a nationwide marketing panel. They were provided point-based incentive for their participation in the study redeemable for prizes. In the survey, participants were presented with visuals of the design artifacts under study as well as a short written description. For each artifact, they were asked to evaluate the extent to which it would support certain activities (FA) and the extent to which it promoted certain goals (SE), e.g., “what goals are being promoted through name of artifact” (SE), “what would you use name of artifact for” (FA). For each artifact, the items for SE were all the 10 items from the card sorting exercise (Table 2). Conversely, the items for FAs, were for each artifact a subset of the 26 items identified in the card sorting exercise (because of space constraints as well as because some FAs were more or less relevant depending on the artifact) (Table 3). Finally, respondents were also asked to estimate the PU, PE, and overall expected value for each artifact. 4.3 Data Analyses and Results 4.3.1 Scale reliabilities – The reliabilities of the PU, PE, FAh, FAu, SEh, and SEu scales were calculated with Cronbach’s alpha for each artifact and were deemed appropriate (all above 0.75, with two values only bellow 0.80). Following that, we calculated the average of these variables and used those scores for the analysis we describe in the following paragraphs. 4.3.2 Regressions on PU and PE (P1 to P4) – For each artifact, we ran two independent regression models, a first one with FAh, FAu, SEh, and SEu determining PU, and a second one with the same independent variables influencing PE (hence, there was a total of 8 regressions, whose results are presented in Table 4). P1 is supported for each artifact as FAu has a strong significant effect on PU and in comparison the effect of FAh on PU is weaker or non significant. P2 is also supported for each artifact as SEu has a significant

Table 4. Results for the regression models analysis

Regressions with PU as a dependent variable

Regressions with PE as a dependent variable

A1 - Product review

Related propositions

Standardized beta

Sig. level


Standardized beta

Sig. level

FAu P1 .494 .000 FAu P3 .074 .471FAh P1 .186 .033 FAh P3 .224 .032SEu P2 .306 .001 SEu P4 .139 .209SEh P2 -.301 .001 SEh P4 .247 .018

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Regressions with PU as a dependent variable

Regressions with PE as a dependent variable

A2 - Online shopper profile


Standardized beta

Sig. level


Standardized beta

Sig. level

FAu P1 .530 .000 FAu P3 .193 .036FAh P1 -.143 .050 FAh P3 .235 .003SEu P2 .532 .000 SEu P4 .432 .000SEh P2 -.137 .073 SEh P4 .061 .456

A3 – Shopper-created product lists

FAu P1 .412 .000 FAu P3 .013 .900FAh P1 .091 .281 FAh P3 .438 .000SEu P2 .523 .000 SEu P4 .446 .000SEh P2 -.159 .028 SEh P4 -.009 .912

A4 – List of online shopping friends

FAu P1 .310 .004 FAu P3 .201 .057FAh P1 .280 .007 FAh P3 .398 .000SEu P2 .457 .000 SEu P4 .229 .032SEh P2 -.209 .024 SEh P4 .088 .330

positive effect on PU and in contrast the effect of SEh on PU is not positive. Surprisingly, the results even indicate a significant negative effect of SEh on PU, indicating that higher hedonic symbolic expressions may be detrimental to perceived usefulness. P3 is supported as FAh has a significant positive effect on PE for each artifact, while in contrast FAu has a lower or insignificant effect on PE. There are unexpected results regarding P4. SEh has a significant effect on PE for one artifact only (A1, i.e., product reviews) while SEu has a significant positive effect on PE for all artifacts except for A1. 4.3.3 Group difference test (P5 and P6) – As in prior work [3], recreational and non recreational shoppers were differentiated based on the extent to which they reported enjoying online shopping. Individuals who chose 4 (i.e., enjoy it) or 5 (i.e., enjoy it very much) on a 1-5 point scale were considered recreational shoppers. We ran t-tests between these two groups and observed that there was a significant difference in the scores for utilitarian functional affordances (FAu) between the recreational shoppers (M=5.63, SD=0.99) and the non recreational online shoppers (M=5.15, SD=1.2) groups (p=0.016, effect size=0.43). The results summarized in Table 5 are consistent across all the other 15 variables examined (FAh, FAu, SEh, SEu - for each artifact), which provides support for P5. Conversely, P6 was not supported as there was no clear and consistent alignment between shopper type and the extent to which shoppers perceive utilitarian and hedonic functional affordances and

symbolic expressions was not supported. Note that while recreational online shoppers (group 1 in Table 5) consistently rated hedonic affordances and symbols higher than utilitarian shoppers (in accordance with P5), they also rated higher the utilitarian affordances and symbolic expressions (which is contrary to P6).

Table 5. Results for the t-tests analysis

A1 N Mean Sig. Effect size FAu 1 86 5.63

0.016 0.43 0 52 5.15

FAh 1 86 4.330.007 0.5

0 52 3.66

SEu 1 86 5.09

0.009 0.49 0 52 4.56

SEh 1 86 3.61

0.061 0.33 0 52 3.19


0.022 0.41 0 52 2.96

FAh 1 86 3.170.015 0.43

0 52 2.42

SEu 1 86 3.88

0.014 0.43 0 52 3.21

SEh 1 86 3.61

0.061 0.6 0 52 3.19


0.071 0.32 0 52 2.43

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FAh 1 86 3.33 0.004 0.5

0 52 2.44

SEu

1 86 3.29 0.003 0.52

0 52 2.48

SEh

1 86 3.56 0 0.64

0 52 2.57


0.045 0.35 0 52 3.03

FAh 1 86 4.26 0.033 0.38

0 52 3.56

SEu

1 86 3.83 0.017 0.43

0 52 3.11

SEh

1 86 4.00 0 0.64 0 52 2.97

Key: 1 refers to the recreational online shoppers group; 0 refers to the non-recreational online shoppers group.

5. Discussion and Conclusion

The results from this study highlight interesting preliminary findings. In a first set of propositions where the affordances and symbols were included in a regression model predicting the behavioral beliefs important in the acceptance of online shopping services (PU and PE), it was found that utilitarian affordances and symbols were strong predictors of the utilitarian individual belief of perceived usefulness. Consistently across the four online social shopping artifacts, FAu and SEu were in each model the two strongest determinants of PU. Interestingly, hedonic symbolic expressions had a negative impact on PU. According to predictions, hedonic affordance was a strong predictor of the hedonic individual belief of perceived enjoyment. However, contrary to our expectations, the hedonic symbolic expressions did not consistently contribute to PE, and utilitarian symbolic expressions had a more consistent and stronger influence on PE. While more evidence is required to depict and isolate the specific influences of FAh, FAu, SEh, and SEu on IT acceptance beliefs and on the overall value online shoppers can gain by using social shopping artifacts, our results bring support to the premise that social shopping artifacts have the potential to trigger both hedonic and utilitarian beliefs through the functional affordances and symbolic expression pathways. The unexpected results obtained for the SEh-PU (significant negative effect for three of the four artifacts), SEh-PE (non significant effect for three of the four artifacts) and SEu-PE (significant effect for three of the four artifacts) relationships could be explained by the difficult operationalization of the

construct of symbolic expressions which still needs to be validated further. Keeping this possible limitation in mind, the results could also imply a potential detrimental (beneficial) effect of hedonic (utilitarian) symbolic expressions. Accordingly, a designer of an online shopping environment would be better off implementing a design whose intent people perceive as utilitarian rather than hedonic. Providing further or stronger practical implications seems difficult given that our results are preliminary.

Results obtained when testing the second set of propositions support Markus and Silver’s recommendation to take the specificities of user groups into account when studying the usage and effects of IT artifacts. We found that recreational online shoppers perceived higher hedonic symbols and affordances than non-recreational shoppers, but surprisingly, they also perceived higher utilitarian symbols and affordances. It is possible that these shoppers are overall more sensitive to artifact’s properties. Nonetheless, as shown in Table 5, the effect sizes for these differences were bigger for hedonic FAs and SEs (i.e., there was, overall, a larger difference in the perceptions of FAh and SEh between the two groups, than for FAu and SEu) which could suggest that despite a tendency of recreational shoppers to perceive higher levels of FAs and SEs, they nonetheless have different perceptions for the hedonic FAs/SEs. These suppositions would deserve further investigations.

The current paper makes the following contributions. First, our attempt to apply Markus and Silver (2008) framework in an empirical study is the first, to our knowledge. Hence, not only have we opened a path to the empirical study of design artifacts conceptualized and measured through the concepts of functional affordances and symbolic expressions [17], but we have also shown that it could generate interesting findings. Second, this study represents a first effort to take social online shopping as a context and to study the hedonic and utilitarian potential of online social design artifacts. We encourage future research to examine the possible complementarities or substitutability of hedonic and utilitarian affordances and symbols, and to study them in a controlled environment (i.e., laboratory experiment). This would help address a limit of this paper as our survey participants reported perceptions after a visual introduction of the artifact, rather than after an interaction with the artifact.

Acknowledgments The authors are grateful for financial support from the Social Sciences and Humanities Research Council of Canada.

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