Thosten Hennig-Thuau, Andé Machand, & Paul Max Can Automated Systems Help Consumes Make Bette Choices? Because hedonic poducts consist pedominantly of expeience attibutes, often with many available altenatives, choosing the ight one is a demanding task fo consumes. Decision making becomes even moe difficult when a goup, instead of an individual consume, will consume the poduct, as is egulaly the case fo hedonic offeings such as movies, opea pefomances, and wine. ting the pevalence of automated ecommende systems as decision aids, the authos investigate the powe of goup ecommende systems that conside the pefeences of all goup membes. The authos develop a conceptual famewok of the effects of goup ecommendes and empiically examine these effects though two choice expeiments. They find that automated goup ecommendes offe moe valuable infomation than single ecommendes when the choice agent must consume the ecommended altenative. Howeve, when agents choose feely among altenatives, the goup s social elationship quality detemines whethe goup ecommendes actually ceate e goup value. Finally, goup ecommendes outpefom decision making without automated ecommendations if the agent s intention to use the systems is. A decision tee model of ecommende usage offes guidance to hedonic poduct manages. Keywods: ecommende systems, joint consumption, hedonic poducts, agents, goup decisions, social elationship quality Choosing the best hedonic poduct epesents a challenging task fo consumes. Conside motion pictues: The abundance of available titles, combined with thei expeience good chaacte, means consumes aely know which offeing will povide the est value. Simila aguments apply to othe hedonic poducts, such as video games, vacation esots, and estauants. To educe the complexity of the choice pocess, automated ecommende systems geneate pesonalized pedictions about poduct liking by filteing the past behavio of and pefeence statements fom consumes (Bodapati 200; Hennig-Thuau et al. 2010). Such systems ae widespead; eseach indicates that they incease customes satisfaction and lead to e sales (Flede and Hosanaga 2009). Scholas and companies continue to seach fo ways to incease thei effectiveness; Thosten Hennig-Thuau is Pofesso of Maketing, Maketing Cente Münste, Univesity of Münste, and Reseach Pofesso of Maketing, Faculty of Management, Cass Business School, City Univesity London (e-mail: thosten@hennig-thuau.de). Andé Machand is Assistant Pofesso of Maketing, Maketing Cente Münste, Univesity of Münste (e-mail: mail@ande-machand.de). Paul Max is a doctoal student, Depatment of Maketing and Media, Bauhaus-Univesity of Weima (e-mail: max@ equestionnaie.de). The authos thank the coopeating ecommende site, Kinowelt/StudioCanal, and paticulaly Jan Rickes and Michael Kölmel fo thei extensive intellectual and logistic suppot of this poject, as well as colleagues Mak B. Houston, Tom Goss, and Manfed Kafft fo thei constuctive feedback. The authos ae indebted to the DFG (Geman Reseach Foundation) fo financial suppot. The authos also thank the anonymous JM eviewes fo thei helpful and constuctive comments on this eseach. Ajay Kohli seved as aea edito fo this aticle. fo example, when the online movie ental company Netflix ecently offeed $1 million to anyone who could incease the pediction accuacy of its ecommende system by 10%, moe than 20,000 eseaches esponded (Loh 2009). Fo this eseach, we take a diffeent oute. In both pactice and eseach, most automated ecommendes focus on an individual consume s pefeences, ignoing a common consumption situation fo hedonic poducts and sevices namely, joint consumption by a goup of consumes. Moe than 90% of movie visits include fiends o elatives (FFA 2011), and simila pecentages ae likely fo leisue tavel, estauant visits, wine puchases, music concets, and so on. Moeove, although consumption takes place jointly, often only a faction of the goup membes paticipates in the actual decision-making pocess, acting as agents fo the othe membes (Weinbeg 2003). Fo example, Weinbeg (2003, p. 24) finds that 3% of [video] entes wee making choices fo people who wee not in the stoe with them. We investigate whethe, in such a context, automated goup ecommendes that conside the pefeences of all goup membes (i.e., the agent and his o he patnes) can help consumes make bette decisions. ecommendes, as we conceptualize hee, use the past atings of altenatives in a poduct categoy by all goup membes (as well as many othe uses of the ecommendation system) to geneate anticipated atings fo poducts that the goup membes have not yet consumed. The esulting ecommendations ae calculated though collaboative filteing o simila appoaches and ae designed to maximize goup value, which we define as the unweighted mean of the value that the membes of the goup deive fom consuming a poduct. 2012, Ameican Maketing Association ISSN: 0022-2429 (pint), 14-1 (electonic) 9 Jounal of Maketing Volume (Septembe 2012), 9 109
This eseach is the fist to investigate the powe of goup ecommendes and assess the value contibution of these aely used systems to consumes and companies. We develop a conceptual famewok of the effects of goup ecommendes and empiically examine these effects. On the basis of the esults, we also offe a decision tee fo manages who cuently offe automated ecommendes o plan to do so. Ou eseach esponds to calls fom maketing and consume eseach scholas to dedicate moe attention to goup consumption pocesses (e.g., Bagozzi 2000; Epp and Pice 200). Regading the effects of goup ecommendes, we ague that in a goup consumption context, goup ecommendes should lead to bette choices than the widespead single ecommende systems that featue only the pefeences of a single goup membe (the agent), not those of any patnes. They should also outpefom conditions in which no automated ecommende is pesent, such as when an agent chooses a poduct solely on the basis of his o he pesonal knowledge of goup membes pefeences. We also conside foces that might modeate the effectiveness of goup ecommendes, namely, the goup s social elationship quality (e.g., Spanie 19) and an agent s intention to use automated ecommendes in the futue (e.g., Baie and Stübe 2010). To test ou popositions, we conduct two laboatoy expeiments that compae the effectiveness of a collaboative filte-based goup ecommende with single and no ecommende conditions. Both expeiments focus on dyads, the most common type of goups in many hedonic industies, and use movies as the poduct categoy; in each expeiment one membe of a dyad acts as an agent and selects a poduct altenative (i.e., a movie). Subsequently, the dyad consumes the poduct jointly, and each membe epots his o he value peceptions. In a esticted-choice scenaio in which agents must pick a top ecommended altenative, the esults suppot ou assetion that, on aveage, goup ecommendes geneate bette ecommendations than single ecommendes. Howeve, this effect fades when agents can choose feely among all altenatives. In this latte case, the supeioity of goup ecommendes depends on the dyad s social elationship quality; goup ecommendes ae moe effective fo dyads chaacteized by social elationship quality. Futhemoe, goup ecommendes outpefom the agent s choices when he o she uses no ecommende, though only if the agent has a intention to use ecommendes in the futue, in suppot of the theoetically poposed bounday conditions. These findings infom a decision tee model that offes manages detailed guidance about the effective uses of ecommendes in geneal and goup ecommendes in paticula. Theoetical Backgound Reseach on Automated s fo Individual Consumes Most existing eseach on automated ecommende systems focuses on developing algoithms that pedict use pefeences with minimal eo, often employing item- o use- based collaboative filteing techniques (e.g., Li et al. 200) o hybid filteing methods (e.g., Liu, Lai, and Lee 2009). In addition to the contibutions of infomation systems scholas (e.g., Koen 2009), maketing scholas have extended the discussion, such as with Ansai, Essegaie, and Kohli s (2000) Bayesian pefeence model and Ying, Feinbeg, and Wedel s (200) model that accounts fo the latent pocesses undelying atings. Bodapati (200) also has intoduced a model based on the sensitivity of customes puchase pobability to ecommendations, and Häubl and colleagues investigate consume behavios elated to the use of automated ecommendes in a seies of studies (e.g., Häubl and Muay 2003; Häubl and Tifts 2000). In geneal, such eseach implies that the intelligent use of collective wisdom by automated ecommendes helps consumes make bette decisions, compaed with constellations without ecommende systems, though little empiical evidence confims this assumption. Studies focusing on algoithms epot elative impovements in pediction eos (e.g., oot mean squaed eo) compaed with benchmak models (e.g., Koen 2009; Ying, Feinbeg, and Wedel 200), but they aely discuss the absolute goodness of fit of the ecommendes. The only study we know of that diectly compaes ecommende vesus no ecommende pedictions is Kishnan et al. (200), who find that, on aveage, automated ecommendes pedict film atings of 14 consumes bette than a sample of 0 human ates, but ecommende pedictions ae bette fo less than half of the consumes (i.e., 43%). Joint Consumption and s Extant eseach on ecommende systems and consume behavio in geneal focuses on individual behavio; goup pocesses have eceived fa less attention. Howeve, joint consumption has at least equal elevance fo seveal poduct categoies, especially hedonic goods (Raghunathan and Cofman 200), and scholas have called fo moe attention to be dedicated to joint consumption (e.g., Bagozzi 2000). In esponse, some maketing eseaches have used nomative and pesciptive goup decision theoy to develop complex decision models, in which a goup seeks to maximize its aggegated utility functions in conditions of uncetainty (e.g., Adamowicz et al. 200). Othe scholas offe econometic models of goup decision making. Fo example, Rao and Steckel (1991) model goup pefeences as a weighted linea combination of individual pefeences and an intecept tem; Aoa and Allenby (1999) study the impact of membe pefeences on goup decisions with a hieachical Bayes model; and Aibag, Aoa, and Kang (2010) model a goup s utility accoding to the initial and evised pefeences of the individual goup membes. Most eseach on joint consumption assumes that goup membes decide jointly, though often only a subgoup of membes might paticipate in the actual choice, acting as choice agents fo othe goup membes (Weinbeg 2003). Some eseach consides elated concepts, such as puchasing agents (consumes who select a poduct fo anothe; e.g., West 199) and suogate shoppes (consumes who take ove unwanted maketplace activities fom othe con- 90 / Jounal of Maketing, Septembe 2012
sumes; e.g., Solomon 19), though in these cases, the agent him- o heself does not consume the puchased choice. Thus, the ole of agents who choose a poduct fo a goup of consumes to which they belong emains unclea. Finally, in the context of ecommende eseach, some compute science scholas have developed pototypes of goup ecommendes. These pototypes include PolyLens (O Conno et al. 2001), CATS (McCathy et al. 200), Adaptive Radio (Chao, Balthop, and Foest 200), and MusicFX (McCathy and Anagnost 199). Some ecent developments even have been implemented by scholas on Facebook (Fun: see Popescu and Pu 2011; Happy Movie: see Quijano-Sánchez et al. 2011). Howeve, no systematic effots have evaluated the pefomance of these systems, and it emains unclea how the integation of goup membes pefeences in the fom of automated goup ecommendes affects consumes value peceptions, paticulaly compaed with single ecommendes o othe infomation souces (e.g., an agent s tacit knowledge of patnes pefeences). We aim to shed moe light on this issue. Conceptual Famewok and Reseach Hypotheses Setting and Oveview of Conceptual Model We study a common hedonic consumption context: A goup, which consists of an agent and one o moe patnes, plans to consume a hedonic poduct such as a movie. The goup gives the agent the mandate to select a specific pod- uct fom a list of available altenatives, with the objective of choosing the altenative that povides the est goup value. As we show in Figue 1, we investigate whethe the infomation povided by goup ecommendes that conside the pefeences of all goup membes geneates e goup value (the unweighted mean of the value that the membes of the goup deive fom consuming a chosen poduct), as well as e value fo the individual membes of the goup namely, the agent (agent value, o the value an agent deives fom consuming the chosen poduct) and his o he patnes (patne value, o the value that a patne deives fom consuming the chosen poduct). We compae these outcomes with the value poduced though othe infomation souces: automated single ecommendes (which only conside the pefeences of the agent, not the patnes) and the agent s own knowledge (without the input of a goup o single ecommende). Finally, we analyze whethe these paths ae modeated by social chaacteistics (the goup s social elationship quality) and pesonal chaacteistics (the agent s intention to use ecommende systems in the futue). Main Effects: Vesus Single and s ecommendes vesus single ecommendes. When applied in a goup consumption context, single ecommende systems, which ae the industy standad fo automated ecommendations, detemine an agent s pefeences and make pesonalized ecommendations fo him o FIGURE 1 Conceptual Famewok of s Infomation Souce Modeatos ecommende Social elationship quality value Single ecommende value ecommende to use ecommendes Patne value Automated Systems / 91
he. When applied in a goup consumption context, a single ecommende thus aims to maximize agent value but does not conside the pefeences of any patnes. In contast, a goup ecommende aims to maximize the goup value peception by ecommending poducts valued by both the agent and his and he patnes. In this sense, goup ecommendes inheently involve a tade-off acoss diffeent goup membes inteests. The agent likely would attain e value fom single ecommendes because goup ecommendes equie the agent to make a compomise that implies a deviation fom his o he value-maximizing choice. Yet the ecommendations geneated by a goup ecommende also should povide moe value fo patnes as a esult of this compomise. Because the goup s value peception by definition involves the value peceived by both agent and patnes, the effect of goup (vesus single) ecommendes on goup value depends on the outcome of this tade-off. If (and only if) the loss in agent value is compensated fo by the gain in patne value, the compomise option that the goup ecommende suggests geneates e goup value than the single ecommende. This outcome depends on the goup ecommende s ability to tansfom the available infomation about agent and patne pefeences in a meaningful way. H 1a and H 1b popose the effects of goup ecommendes on patnes and the agent, espectively. H 1c then summaizes ou expectation that goup ecommendes offe ecommendations that incease patne value moe than they educe agent value and, by doing so, outpefom single ecommendes when a hedonic expeience poduct is consumed by a goup instead of an individual consume. H 1 : Automated goup ecommendes offe (a) e patne value, (b) e agent value, and (c) e goup value than single ecommendes. ecommendes vesus no ecommendes. Automated ecommendes in geneal povide pesonalized suggestions, dawn fom infomation povided by a lage numbe of othe (usually anonymous) consumes. Theefoe, goup ecommendes should poduce e-quality decisions than those that aise when no automated ecommende is available, such as when the agent makes a choice solely on the basis of his o he own pesonal knowledge of goup membes pefeences (the no ecommende constellation). Specifically, the ecommende equips the agent with collective intelligence (Lévy 199) that is, infomation about poduct pefeences deived fom a multitude of othe consumes; such infomation is not available to the agent when he o she has no access to a ecommende system. Howeve, consumes often have geat talent to daw and intepet soft infomation about othe consumes a skill lacking in the algoithms used to geneate automated ecommendations. That is, the ecommende s lack of such soft skills could lead to suboptimal ecommendations that, if adopted by the agent, could poduce e goup value than decisions based solely on soft infomation. Oveall, howeve, we expect that the combination of access to collective intelligence fom goup ecommende esults and pesonal soft infomation will lead to bette decisions by the agent. Thus: H 2 : Poduct choices made by an agent with access to a goup ecommendation system esult in e goup value than those made without a ecommendation system. Inteaction Effects: Social Relationship Quality and to Use s Social elationship quality. The supeioity of goup ecommendes ove single and no ecommendes might vay with the quality of the social elationships among goup membes, that is, the goup s social elationship quality. Social elationship quality, defined as the qualitative evaluation of a social elationship (Spanie 19), is a wellestablished and widely studied constuct in social psychology eseach (e.g., Collins and Read 1990; Kudek and Schmitt 19). 1 Social psychologists offe diffeent conceptualizations of the constuct (Fletche, Simpson, and Thomas 2000); fo this eseach, we employ Spanie s (19) popula appoach. Spanie models social elationship quality accoding to thee dimensions: satisfaction with maiage, consensus, and cohesion. His model clealy focuses on a specific context (i.e., maied couples), so we adapted these dimensions to ou study context, dawing on extant social elationship quality eseach to find elated concepts that peviously have been modeled as facets of elationship quality. Specifically, we use elational liking (the degee of intepesonal attaction; Collins and Read 1990) instead of satisfaction with the maiage, peceived elational similaity (the ovelap of peceptions, attitudes, and values between patnes; ton 193) instead of consensus, and peceived elational closeness (the degee to which patnes shae thei inmost feelings and thoughts; Aon, Aon, and Smollan 1992) instead of cohesion. With egad to the poposed modeating ole of social elationship quality, we expect goup ecommendes to have a stonge positive effect on goup value when elationship quality is than when it is. ecommendes povide value based on the assumption that goup membes in geneal, and the agent in paticula, seach fo a compomise (i.e., the tade-off of pesonal pefeences against the pefeences of othe goup membes); theefoe, it is impotant that the goup membes ae willing to accept such a compomise. We anticipate that they ae willing to do so when the goup s social elationship quality is, such as when goup membes stongly like one anothe. Howeve, goup membes may be less willing to compomise, and value the compomise choice offeed by goup ecommendes less, when thei goup s social elationship quality is. An agent who chooses a compomise poduct (which optimizes not his o he own pefeences but athe the goup s as a whole) should be satisfied with the selection, as should the patnes (whose pefeences ae consideed in the selection pocess), if they enjoy social elationship quality. In contast, if the goup s social elationship quality 1In social psychology, this constuct is mostly efeed to as elationship quality, but we pefe the tem social elationship quality to distinguish ou constuct fom eseach on commecial elationships between customes and fims that also uses the tem elationship quality to descibe a diffeent phenomenon (e.g., Palmatie et al. 200). 92 / Jounal of Maketing, Septembe 2012
is, the agent suffes advese consequences of a compomise choice that includes the pefeences of patnes whom the agent likes less. Even if these patnes appeciate the choice to the same degee, goup value (i.e., an aggegate of all goup membes value peceptions) will be e in the social elationship quality condition. Futhemoe, because the consumption takes place jointly, the agent s positive o negative peceptions of the compomise option might stengthen patnes peceptions of the consumption expeience though social influence effects (e.g., Bohlmann et al. 200; Raghunathan and Cofman 200) and emotive pocesses (e.g., Hatfield, Cacioppo, and Rapson 1994). Thus: H 3 : The impact of goup vesus single o no ecommende systems on goup value is modeated by the goup s social elationship quality, such that goup ecommendes ae moe effective when the goup s social elationship quality is (vs. ). to use automated ecommendes. The effectiveness of goup ecommendes also may depend on individual agent chaacteistics. Specifically, we build on eseach that has shown that an agent s attitudes about and intentions to use automated ecommendes (conative attitude) vay acoss consumes (Baie and Stübe 2010; Fitzsimons and Lehmann 2004; Hu and Pu 2009). 2 If an agent does not believe that automated ecommendes ae valuable infomation souces and has no intention to use them in the futue, he o she deives less value fom using them than an agent who holds a positive attitude towad ecommendes and intends to use them in the futue. We ague that agents with negative attitudes towad automated ecommendes will devalue the infomation povided by the ecommende system and eithe discount this infomation o expess bias against it by ating ecommended choices less positively. This behavio should also affect patne value because the agent s choices will be less effective fo the goup, and negative sentiment again speads though social influence and emotional contagion (Hatfield, Cacioppo, and Rapson 1994; Raghunathan and Cofman 200). If the agent instead holds a positive attitude towad ecommendes and intends to use them in the futue, these detimental effects should not occu, which will lead to e goup value. Thus: H 4 : The impact of goup vesus no ecommende systems on goup value is modeated by the agent s intention to use ecommende systems, such that goup ecommendes ae moe effective when the agent s usage intention is (vs. ). Testing the Hypotheses: Two Expeiments To test the hypothesized elationships, we conducted two expeiments. In the fist expeiment, we esticted agents choice by filteing out influences that wee not elated to the ecommendations, so that we could compae the value potential inheent to goup vesus single ecommendes 2The intention to use a ecommende system constuct does not diffe between the goup and single ecommende conditions, so we conside it only fo the goup vesus no ecommende compaison. diectly (esticted choice design) and test ou fist hypothesis. This design implies a eduction of choice fo paticipants, so we contolled fo a potential effect of agents eactance (i.e., a motivational state diected towad eattaining the oiginal feedom of choice; Behm 19) to the choice design, which might bias paticipants value peceptions (Fitzsimons and Lehmann 2004). In the second expeiment, agents could choose feely among altenatives (fee choice design). This study was necessay because H 2 H 4 involve a compaison between a goup ecommende and a no ecommende condition. Because it is essential fo the latte condition that agents can choose feely among available altenatives (solely on the basis of thei pesonal knowledge), compaing such a fee choice with a esticted choice condition fo the ecommende scenaios would have disadvantaged the latte and caused biased esults. Moeove, testing the modeating effects of social elationship quality (H 3 ) also equies the agent to have discetion, which was not the case in the fist expeiment. In both expeiments, we used movies as the poduct categoy because they ae pedominantly consumed in goups and difficult to assess in advance (De Vany and Walls 1999). In addition, the type of consume agency behavio implied in ou theoetical aguments is an established phenomenon in the movie context (Weinbeg 2003), and movies ae a standad setting fo automated ecommendes in both academia (e.g., Ansai, Essegaie, and Kohli 2000) and pactice (e.g., Netflix). In both expeiments, we studied dyads (i.e., goups of two pesons), consistent with pevious eseach on goup decisions (e.g., West 199). In addition to limiting the complexity of the expeiments, a two-peson goup size has pactical elevance because it epesents the dominant constellation fo hedonic poduct categoies, including motion pictues (FFA 2011). Expeiment 1: Resticted Choice Sample and pepaatoy actions. Paticipants wee ecuited though postes, website announcements, e-mails, and pesonal communication thoughout the campus of a lage public Geman univesity; they consisted mostly of (gaduate) students. Each paticipant who egisteed at the specific univesity website to paticipate in a study on consume satisfaction with movies had to povide contact details fo him- o heself and fo a patne who had ageed to paticipate. Both patnes then eceived a unique identifie, such that they could set up an account with a popula Geman movie ecommende website and ate a minimum of 0 movies. At the time of the expeiment, the actual website contained appoximately 4. million atings of some 40,000 movies by moe than 30,000 active uses. Each paticipating dyad could eceive two movies on DVD (the one selected in the expeiment and an additional title they selected afte thei successful paticipation), as well as exta couse cedit, as compensation. Among the 214 paticipants who took pat (10 dyads; goup ecommende = 2 dyads, single ecommende = dyads), the aveage age was 24.4 yeas, and 3.4% wee women. Table 1 povides futhe desciptive infomation. Automated Systems / 93
Design. Registeed paticipants wee andomly assigned to be agents o patnes in thei dyad. paticipants visited the expeimental lab, without thei patnes, whee they wee asked to select a movie that they and thei patne had to watch togethe within two weeks of thei choice. The 24 available movies wee equally distibuted acoss six common genes (action, dama, hoo, love stoy, comedy, TABLE 1 Sample Desciption Expeiment 1 Expeiment 2 s Patnes s Patnes Total Paticipants 10 10 123 123 Gende Female 2 (2%) 3 (0%) (%) (2%) Male 0 (%) 4 (0%) (4%) 4 (3%) Age (in Yeas) Range 21 31 19 1 0 1 M 24.10 24.1 2.41 2.9 Mdn 24 24 24 24 SD 1..09.. Occupation Student 9% 4% 3% 2% Employed 2% 1% 1% 2% Unemployed 0% 1% 2% 1% Social Relationship Quality (0 ) Range 2.2.00 2.3.00 2.00.00 2.1.00 M.1.2.9.4 Mdn...13.0 SD 1.00.9.. Movie Rating (0 10) Range.0 10.0.0 9..0 9..0 10.0 M.1 4..4.3 Mdn.00.0.00.00 SD 2.9 2.3 2.00 2.30 Towad Usage (0 ) Range N.A. N.A. 1.0.0 N.A. M N.A. N.A. 4. N.A. Mdn N.A. N.A..00 N.A. SD N.A. N.A. 1.2 N.A. Reactance (0 ) Range..0 N.A. N.A. N.A. M 3. N.A. N.A. N.A. Mdn 3. N.A. N.A. N.A. SD 1.3 N.A. N.A. N.A. tes: N.A. = not applicable. and science fiction). The selection of available movie titles esembled a consume s choice set of theatical movies in a medium-sized city. Most titles wee ecent theatical eleases, though one o two movies pe gene wee classic o independent/at house titles. We intentionally excluded blockbuste titles because the expeiment excluded fom consideation any movies that a goup membe had seen peviously, so blockbustes likely would have educed the numbe of available altenatives. When aiving at the lab, each agent was assigned andomly to one of two expeimental conditions, goup ecommende o single ecommende, which we descibe in detail in the next section. The agents indicated which of the 24 movies they had seen o wee cetain that thei patnes had seen; these movies, as well as those that the agent o patne aleady had ated on the ecommende site, wee automatically eliminated fom the list to avoid bias fom pevious consumption expeiences. The patne was not involved in the decision making, and no communication between agent and patne was possible duing the choice pocess. The agents then made a choice based on the individualized esults of the goup ecommende o single ecommende, espectively. In both conditions, they had to choose one of the thee movies with the est ecommendation (esticted choice). Afte having selected a movie, the agents completed a questionnaie about thei film pefeences, the movie selection pocess undegone duing the expeiment, and additional details about themselves and thei patnes. To account fo potential effects of eactance as a eaction to the esticted choice design, the questionnaie also included a measue of the agent s eactance to the esticted choice. The eactance constuct then seved as a covaiate in the analysis to ensue that value diffeences esulted not fom the choice estiction but fom the expeimental manipulation. Afte finishing the questionnaie, the agents eceived a DVD of the selected movie, which they watched with thei patnes at a place of thei choice. Afte the joint movie consumption act, both the agent and the patne completed an online questionnaie that asked them how much they liked the movie the agent had selected (i.e., agent value and patne value, fom which we calculated goup value). Expeimental conditions. Table 2 details the diffeent steps fo the paticipants in both expeimental conditions. In the single ecommende condition, the agent was povided Condition (Expeiments 1 and 2) (a) Log in (b) Exclude aleady seen movies (c) Weight I (without visible movie atings) (d) Weight II (with visible movies atings) (e) Choose movie with goup ecommendations (f) Confimation of chosen movie TABLE 2 Expeimental Pocess Steps Single Condition (Expeiments 1 and 2) (a) Log in (b) Exclude aleady seen movies (c) Visible single ecommendations (d) Choose movie with single ecommendations (e) Confimation of chosen movie Condition (Expeiment 2) (a) Log in (b) Exclude aleady seen movies (c) Choose movie without any ecommendations (d) Confimation of chosen movie 94 / Jounal of Maketing, Septembe 2012
with the Geman title, county of oigin, main gene, and a mini-poste fo each available movie in a closed-beta envionment of the ecommende site. By clicking on a title o mini-poste, the agent gained access to additional infomation fo each title, such as a lage movie poste; the oiginal movie title; a plot summay; a listing of the main actos, diecto, and wite(s); and the movie s un time. In addition, the agent obtained thee atings fo each film: the aveage movie ating of all membes fom the ecommende site, the aveage movie ating fom pofessional movie citics on the site, and the agent s pesonalized movie ating pedictions, geneated on the basis of his o he pevious atings of othe movies on the site (Figue 2). To calculate the pesonalized movie ating pedictions, this expeiment used a memoy-based collaboative usefilteing method, which measued pefeence similaity between uses accoding to Euclidean distances: ( ) = ( ) (1) d A, U a u, i= 1 whee a i and u i ae atings by the agent A (i.e., consume who eceives the ecommendation) and use U (i.e., povide of atings fo deiving the ecommendation fo A) fo all movies I ated by both A and U. When calculating ecommendations fo A, all uses U who ated at least 10% of the movies A had ated and exhibited a pefeence similaity d(a, U) of less than.0 epesented neighbos. The pediction equaled the unweighted mean value of all atings by neighbos fo a movie i. The movies pesented to the agent appeaed in descending ode, accoding to the pesonalized ating pedictions. n i 2 i To limit the impact of othe factos that might influence consumes choices in eality (e.g., mood), agents had to select one of the thee movies that eceived the est pedicted atings. This esticted choice design elates conceptually to adaptive pesonalization systems, in which uses view only those poducts selected by a ecommende algoithm (which then uses the time a poduct is consumed as a poxy fo its utility; Chung, Rust, and Wedel 2009). Howeve, we did not foce the agent to select the single top ecommended title because this might have poduced atificial esults (e.g., nonomantic fiends being foced to watch a omantic movie togethe). We admit that the focus on intenal validity might come at the cost of educed extenal validity because, in eality, consumes almost always choose feely whethe to fol a ecommendation o select a diffeent movie. We addessed this concen with ou second expeiment, which focuses on extenal validity. In the goup ecommende condition, we pocessed the movie atings povided by both the agent and the patne, befoe the choice situation. In anothe specific closed-beta envionment on the ecommende site, the agent detemined his o he pefeence weights fo both goup membes to calculate a goup ecommendation. Specifically, he o she assigned a weight on two slide bas (one fo him- o heself and one fo the patne) that anged fom 0% to 100%. The slides wee intedependent; the sum of the two values always equaled 100. 3 3Foty pecent of the agents in the fist expeiment (and 4% in the second expeiment) did not change the standad setting of weights (i.e., fom 0 0). FIGURE 2 Sceenshot fo Movie Choice in Single Condition tes: In the oiginal display, the language was Geman. Fo bette eadability, we changed it to English in this figue. Automated Systems / 9
The agent then studied the list of available titles, which contained the same infomation povided in the single ecommende condition: cast, cew, content, value pediction fo the agent (calculated with collaboative use filteing, as in the single ecommende condition), aveage viewe ating, and aveage citic ating. In this condition, the agent also eceived a value pediction fo the patne (calculated fom the patne s atings with the same method used fo the agent) and a goup value pediction, which was calculated as the mean of the agent and patne value pedictions, weighted with the espective pefeence weights (Figue 3). Finally, the agent selected a movie fom the list; as in the single ecommende condition, his o he choice was esticted to one of the thee movies with the est pedicted goup value. Model and measues. value seved as the dependent vaiable fo most analyses. Thee is no single, natual way to deive a goup s joint value fom goup membes individual value peceptions, so we foled extant eseach and used the unweighted mean of agent value and patne value as ou measue of goup value. Masthoff (2004) confims that aveage stategies ae most common fo small goups such as dyads. 4 We measued agent and patne 4We also obtained evidence of the adequacy of this opeationalization fom egessions that we an using altenative measues of goup value (e.g., minimum value, mean value with standad deviation coection). Although the esults emained stable, the vaiance explanation was est fo both expeiments when we used the mean of agent value and patne value as ou measue of goup value. value on a scale that anged fom 0 ( vey bad ) to 10 ( excellent ) in half-point steps. A simila scale appeas on seveal popula online movie sites (e.g., the Intenet Movie Database), including the site we used fo this study. Both goup membes povided this ating afte watching the film. As independent vaiables, Expeiment 1 included the goup dummy and the eactance contol. To measue eactance, we used fou items fom Deci et al. (1994), Hong and Faedda (199), and Unge and Kenan (193), which we epot in Appendix A. Results. In H 1, we popose that goup ecommendes offe e goup value than single ecommendes. Because the goup vaiable is categoical and the eactance covaiate is metic, we used an analysis of covaiance to test this poposition. When we compaed the goup value in the goup vesus single ecommende conditions, we found a significant diffeence fo goup value (x =., x Single = 4.33; F(1, 10) =.21, p <.01, 2 =.0), in suppot of H 1c. Consistent with ou theoetical aguments fo H 1a, this effect was mainly based on the patne s value peceptions (whose pefeences wee not taken into account by the single ecommende system). The patne s value peception diffeed significantly between the goup and single ecommende conditions (x =.4, x Single = 3.92; F(1, 10) = 10.09, p <.01, 2 =.09). Howeve, wheeas H 1b pedicted that the agent s value peception would be e fo goup ecommendes, we found that it diffeed only slightly and that the diection of the effect even an counte to ou expectations. value tended to be e in the goup ecommende condition, afte he o she took the patne s pefeences into account (x =.4, x Single = 4.; F(1, 10) = 2.99, p = FIGURE 3 Sceenshot fo Movie Choice in Condition tes: In the oiginal display, the language was Geman. Fo bette eadability, we changed it to English in this figue. 9 / Jounal of Maketing, Septembe 2012
.0, 2 =.03). We speculate that goup membes who consumed the poduct jointly influenced each othe s peceptions and value assessments. As an aside, we note that the covaiate of eactance was significant at p <.0 fo the agent (F = 4.03, 2 =.04), the patne (F = 4.0, 2 =.04), and the goup (F = 4.9, 2 =.0), with e eactance associated with e value peceptions. Expeiment 2: Fee Choice Method and expeimental conditions. In tems of context and design, the second expeiment lagely eplicated the fist but offeed a few key diffeences. Most impotant, the agent could choose feely among available movies, in contast with the esticted choice in the fist expeiment. Although this fee choice design intoduced noise because agents wee influenced by not only the ecommendations but also thei idiosyncatic chaacteistics it enabled us to test H 2 H 4. Futhemoe, this second expeiment included the poposed modeato vaiables and a no ecommende condition. The goup and single ecommende conditions emained the same as in the esticted choice expeiment. The questionnaies vaied just slightly: The pe-viewing suvey excluded the eactance items (because the agent could chose feely this time) but included measues of the modeatos, namely, the agent s peception of social elationship Specifically, a median split analysis poduced the foling esults: eactance sample = x =.3, x =.90, and x Patne =.1; eactance sample = x = 4.0, x Single = 4., and x Patne = 4.33. quality and his o he intentions to use ecommendes in the futue. In the no ecommende condition, the agent eceived the title, county of oigin, main gene, and a mini-poste fo each movie, as well as the enhanced infomation if he o she clicked on the title o poste, and had to make his o he choice solely on the basis of this infomation, with no access to othe websites o consume opinions duing the selection pocess. In this condition, the movies appeaed in andom ode (Figue 4). Sample. The egistation pocess was the same as in the fist expeiment, and the oles of agent and patne wee again andomly assigned within each egisteed dyad. Paticipants included students fom a diffeent public Geman univesity than that in the fist expeiment. The 24 paticipants (123 dyads) wee andomly assigned to one of the thee expeimental conditions: 3 dyads took pat in the no ecommende condition, 43 took pat in the goup ecommende, and 43 took pat in the single ecommende condition. Table 1 povides desciptive infomation about this sample. Models and measues. In line with ou theoetical aguments, we an thee kinds of analyses: an analysis of vaiance in which we compaed value peceptions between the goup and no ecommendes (to test H 2 ) and two sets of egessions in which we tested H 3 and H 4. In the fist egession, we compaed the goup and single ecommende conditions by egessing goup value (plus agent value and patne value in additional estimations) on the goup ecommende dummy, social elationship quality, and the social elationship quality goup ecommende inteaction tem. In the second egession, compaing the goup FIGURE 4 Sceenshot fo Movie Choice in Condition tes: In the oiginal display, the language was Geman. Fo bette eadability, we changed it to English in this figue. Automated Systems / 9
and no ecommende conditions, we egessed the same dependent vaiables on the agent s intention to use ecommendes and an intention to use goup ecommende inteaction tem, in addition to the independent vaiables. Fomally, the models ae as fols: (2) GV = 0 + 1 GR + 2 + 3 GR +, and (3) GV = 0 + 1 GR + 2 + 3 GR + 4 IN + IN GR + whee GV is goup value, GR is the goup ecommende condition (vs. the single ecommende condition in Equation 2; vs. the no ecommende condition in Equation 3), is the agent s peception of social elationship quality, and IN is the agent s intention to use automated ecommendes in the futue. Ou opeationalizations of agent value, patne value, and goup value matched those fom Expeiment 1. We used eight items to measue the thee dimensions of social elationship quality; specifically, we measued elational liking with thee items fom Wayne and Feis (1990) and Liden, Wayne, and Stilwell (1993); peceived elational similaity with thee items fom the same authos; and peceived elational closeness with two items fom Aon, Aon, and Smollan (1992). In the egessions, we used a composite measue of social elationship quality that eflected its thee-dimensional chaacte. Specifically, we fist calculated the mean of the items fo liking, similaity, and closeness, espectively, and then detemined the oveall mean value of the thee dimensions. Finally, the measue of the agent s intention to use a ecommende was an item fom Maheswaan and Meyes-Levy s (1990) attitude-towadbehavio scale that captues a consume s conative attitude towad an object. To model the inteaction effects, we adopted Lance s (19) esidual centeing appoach to minimize potential multicollineaity, foling Bottomley and Holden (2001) and Hennig-Thuau, Houston, and Heitjans (2009), among othes. Residual centeing is an effective and consevative test fo inteaction effects that assigns only the pat of the vaiance that is not explained by the main effects to the inteaction tem. We epot these items in Appendix A. Reliability and validity. The Conbach s alpha values anged between.4 and.91 fo the thee social elationship quality dimensions, which is satisfactoy (e.g., Chuchill 199). A confimatoy facto analysis fo the thee social elationship quality dimensions model showed a good fit (nomed fit index [NFI] =.9, confimatoy fit index [CFI] =.9, and oot mean squae eo of appoximation [RMSEA] =.0) and was supeio to a competing one-facto social elationship quality model (NFI =.0, CFI =.1, and RMSEA =.32). Similaly, Fletche, Simpson, and Thomas (2000) show that the best-fitting model fo social elationship quality is one in which the items load on fist-ode factos, which in tun load on a second-ode facto that eflects oveall social elationship quality. Results. We tested H 2, which postulated that goup ecommende infomation would incease goup value, with a one-way analysis of vaiance that compaed the goup and no ecommende conditions. Contay to ou expectations, goup value fo the no ecommende condition was not significantly diffeent fom that in the goup ecommende condition, and the aveage goup value was even slightly e (x =., x =.0; F(1, ) =.11, p >.10). The same finding applied to both agent value (x =.4, x =.9; F(1, ) =.4, p >.10) and patne value (x =.4, x =.4; F(1, ) =.00, p >.10). In othe wods, the additional infomation povided by goup ecommendes did not incease the quality of the agent s choices on aveage. Thus, we eject H 2. We next estimated Equations 1 and 2 using odinay least squaes egession (fo esults, see Table 3). In the fist egession model, we compaed the goup ecommende condition with the single ecommende condition. The R-squae values wee.1 fo the goup,.1 fo the agent, and.14 fo the patne. The effect of the social elationship quality goup ecommende inteaction tem was positive and significant (p <.01) fo goup value, as well as fo the individual values of the agent (p <.01) and the patne (p <.01), in full suppot of H 3. To gain a deepe undestanding of this inteaction and the undelying pocesses, we examined the slopes of the ecommende conditions fo and levels of social elationship quality, an appoach that Fitzsimmons (200) efes to as spotlight analysis (because it tuns a spotlight on paticula egions of inteest). Specifically, this analysis involves shifting the mean level of the modeato vaiable up and down by one o moe standad deviations (Fitzsimmons 200, p. ) and then conducting significance tests fo an individual slope, without making any abitay dichotomous assignments, as occus fo a median split (see also Aiken and West 1991). We conducted this spotlight analysis at one and two standad deviations in all cases. In Figue, we pesent the slope plot of the inteaction (one standad deviation above and be the mean). Table 4 contains the detailed esults. As Figue shows, the slope plot suppots ou theoetical aguments: The goup ecommende led to moe goup value than the single ecommende when the quality of the social elationship was than when it was. The spotlight analysis shows that this incease of value in the elationship quality constellation is significant fo both the agent and the patne at p <.0 at one standad deviation. The slopes also show that in the elationship quality constellation, dissatisfaction with the compomise the goup The fee choice design of this expeiment meant that in the ecommende conditions, some paticipants did not choose the top ecommended altenative. The choice to ignoe (o fol) ecommendations is a continuous one because paticipants eceived a ank-odeed list of ecommended titles, so evey paticipant s choice should have been influenced by the ecommendations. Limiting the sample to choices of the top one o top thee ecommended films would be somewhat abitay, and it would educe the sample size and model powe. Futhemoe, when we ean ou egessions fo diffeent subsets, the esults wee consistent with ou full sample esults; the R-squae value geneally inceased somewhat, while the significance levels deceased due to the smalle sample size. 9 / Jounal of Maketing, Septembe 2012
TABLE 3 Regession Results Vesus Single Vesus Patne Patne Regesso B Beta t (p) B Beta t (p) B Beta t (p) B Beta t (p) B Beta t (p) B Beta t (p) Automated Systems / 99 Constant 3.290 4.12 2.4 2.1 2.44 2.292 GR.11.03.3.023.00.0.29.01.0.0.021.20.23.0..03.013.13.2.229 2.2 *.421.13 1.9.30.242 2.3*.0.29 2.4*.1.229 2.1 *.44.21 2.33* GR 1.00.341 3.39 ** 1.0.342 3.3** 1.49.20 2.4** 1.01.230 2.1* 1.033.22 2.11 * 1.0.204 1.9 IN N.A. N.A. N.A..1.11 1.21.243.1 1.3.0.04.41 IN GR N.A. N.A. N.A..1.213 2.02*.440.1 1..1.22 2.10* R 2.10.14.140.21.20.1 R 2 adjusted.139.11.10.1.12.131 *p <.0. **p <.01. tes: N.A. = not available. GR = goup ecommende, = social elationship quality, and IN = intention towad ecommende usage.
FIGURE Slopes fo Social Relationship Quality Inteaction (Expeiment 2) vs. vs. Single Single vs. Single vs. vs. vs. A: vs. vs. Single Single vs. vs. Single Single vs. vs. vs. hig hig h hig h hig.1.1.1.1.1.1.0.0.0.0.0.3.3.3.0.3.3.3.2 Va lue.4.4.4.4.4.4.0.0.0.0.0.0............ Sin Single gle Single n Si Si gle gle R R ecommende R ecommende ecommende e commende commende Reco Reco Reco mmende mmende Reco mmende mmende.19.19.19.19.19.19 Va lue.12.12.12.12.12.12.3.3.3.3.3.3.1.1.1.1.1.1.02.02.02.02.02.02.4.4.4.4.4.4.......3.3.3.3.3.3 Single n Single G R G eoup R ecommende ecommende Single Si Si oup Reco mme gle gle G oup R eoup oup commende commende commende R G oup Reco G ecommende G oup Reco mme G oup mme Reco nde mme nde.4.4.4....4.4.4... Patne Patne Patne Patne Patne Patne.4.4 Patne Patne.44.44.4 Patne.44.4 Patne.44.4.4 Patne Patne.44.44 Va lue.43.43.43.43.43.43.99.99 lo w.99.99 lo.99.99 lo lo.43.43.43.0.0.0.43.43.43.0.0.0 Si Single n gle Si ngle G oup R G eoup commende N o R ecommende ecommende N o R G oup oup Reco Reco G mmende mmende Si ngle Si Si gle gle G oup R eoup oup commende commende commende N o ecommende ecommende G oup Reco oup oup mmende Reco Reco mmende mmende tes: = social elationship quality. Patne Patne B: vs. ecommende suggested was not limited to the agent but also emeged fom the patne, suppoting the poposed existence of social influence and contagious effects within the goup. The spotlight analysis povides evidence that the decease in value in the elationship quality constellation was significant at p <.0 fo both the agent (at one standad deviation) and the patne (at two standad deviations). The compaison between goup and single ecommendes in the fee choice context also evealed notable insights egading the main effect of the goup ecommende vaiable. Although the goup value fo the goup ecommende condition was e than that fo the single ecommende condition (x =., x Single =.43), the diffeence was smalle than in the esticted choice expeiment and insignificant (F(1, 4) =.0, p >.10). The diffeences wee not significant fo any goup membe, though they tended to be e fo the patne. In othe wods, the main effect of goup vesus single ecommendes, which was significant in the esticted choice context, was no longe significant when consumes could choose fom a vaiety of options that is, when we added extenal noise to the expeimental design. In the fee choice scenaio, agents could coect dubious suggestions the ecommende povided; using thei own judgment, they poba- 100 / Jounal of Maketing, Septembe 2012
TABLE 4 Results of Spotlight Analyses Patne Setting and Slope t(1sd) t(2sd) Significance t(1sd) t(2sd) Significance t(1sd) t(2sd) Significance Automated Systems / 101 Vesus Single Recommendation System and Single Recommendation System at High 2.9 3.21 2.43 3.04 2.3 2.3 at Low 2.1 2.9 2.3 3.00 1. 2.20 at Recommendation System 3.90 3.90 3.42 3.42 3. 3. at Single Recommendation System 1.21 1.21 n.s. 1. 1. n.s... n.s. Vesus Recommendation System and Recommendation System at High 1. 2.23 1.2 1. n.s. 1. 2.20 at Low 2.0 2.4 2.21 2.4 1. 2.11 at Recommendation System 3.9 3.9 3.4 3.4 3.44 3.44 at Recommendation System.2.2 n.s..22.22 n.s..2.2 n.s. and Recommendation System at High IN 1.2 2.14 1.0 1.0 n.s. 1. 2.31 at Low IN 1.92 2.33 1.4 2.0 1.2 2.22 IN at Recommendation System 2. 2. 2.94 2.94 2.42 2.42 at Recommendation System.0.0 n.s..10.10 n.s. 1.13 1.13 n.s. Significant (p <.0) fo two standad deviations. Significant (p <.0) fo one and two standad deviations. tes: n.s. = not significant. = social elationship quality, IN = intention towad ecommende usage. The t-values ae equal fo slopes at goup ecommendation system and single/no ecommendation system because these ae dichotomous and thus SD-independent vaiables.
bly oveuled poo suggestions that single ecommendes povided. Consistent with this agument, the numbe of paticipants who foled ecommendations was e in the goup ecommende condition than in the single ecommende condition. Specifically, 23% of the paticipants chose the top ecommendation the goup ecommende offeed, compaed with 12% in the single ecommende condition. The esults wee simila fo the top thee (goup: 49%, single: 40%), top five (goup: 0%, single: %), and top seven (goup: 9%, single: 0%) ecommendations. In the second egession model, we compaed the goup ecommende condition with the no ecommende condition, in which agents made choices solely on the basis of thei own knowledge about the patne. We found R-squae values of.22 fo the goup,.21 fo the agent, and.19 fo the patne. As in the fist egession, the effect of the social elationship quality goup ecommende inteaction was positive and significant fo goup value (p <.0) and fo the agent (p <.0); howeve, it only appoached statistical significance fo the patne (p =.0). In geneal, the spotlight analysis evealed the same patten fo the goup vesus no ecommende than fo the goup vesus single ecommende compaison. The value incease elated to goup ecommendes fo social elationship quality was significant at p <.0 fo both the goup and the patne at two standad deviations (but did not each significance fo the agent). The decease in value though goup ecommendes fo social elationship quality was significant fo the goup and the agent at one standad deviation and fo the patne at two standad deviations. The slope gaphs, epoted in Figue, point to one diffeence between the compaisons (which is eflected in somewhat e betas fo the goup vesus single compaison): peception in the social elationship quality constellation is e fo single than fo no ecommendes. This e value peception might be attibuted to the agents dissatisfaction with the single ecommende s choice, which ignoed thei close patnes pefeences when elationship quality was ; they wee less concened if thei social elationship had e quality. In H 4, we popose that the impact of goup ecommende infomation on goup value would vay with the agent s intention to use ecommendes. Consistent with ou expectations, the inteaction effect of intention towad ecommende usage and goup ecommende was positive and significant fo goup value (p <.0), in suppot of H 4. The inteaction effect of intention to use ecommendes was stonge fo the patne (significant at p <.0) than fo the agent (not significant). Pehaps when making choices in the no ecommende condition, the agent could compensate on aveage fo the value potential of the goup ecommende by choosing a film that met his o he own pefeences. We also noted a diect effect of intention fo the agent but not fo the patne. In Figue, we plot the slopes fo this inteaction. We again conducted a spotlight analysis. Table 4 pesents the significance tests fo the intention to use ecommendes vaiable. The slopes fo intention to use ecommendes illustate that the patne deived e value fom the chosen movie FIGURE Slopes fo to Use Inteaction (Expeiment 2)...91..91.91.3.3.3.9.9.9 R..99..99..99.92.92.92... R R ecommende.44.44. Patne.44 Patne.. Patne.0.0.2.0.2.2 R G R ecommende G oup in the goup R ecommende ecommende G oup condition than in the no ecommende condition when the agent held a positive attitude (i.e., usage intention) towad the ecommende system; the diffeence was significant at two standad deviations. The agent makes use of the value potential povided by the ecommende, as a esult of his o he positive attitude towad ecommendes. In contast, when the agent had no intention to use a ecommende, the agent and patne both deived less value fom the movie that had been chosen in Patne 102 / Jounal of Maketing, Septembe 2012
accodance with the povided ecommendations; both effects wee significant at two standad deviations. The e patne value likely esulted fom the agent s decision, which excluded the goup value potential povided by the goup ecommende. The e value fo the agent might eflect a combination of distaction due to goup ecommendations duing the choice pocess and ejection of the movies suggested by the ecommende, such that the agent had to povide atings to achieve a sense of consistency. Finally, the egession esults shed new light on H 2. The poposed positive effect of goup ecommendes on goup value, though not elevant in all situations, emeged in situations in which agents wee chaacteized by thei positive attitude towad ecommendes. In othe wods, ou ejection of a geneal effect of goup ecommendes can be attibuted pimaily to agents who do not think ly of automated ecommendes and in pactice would hadly use them. Post hoc analysis of social elationship quality effects. Because social elationship quality is a multidimensional constuct, we also investigated how its dimensions (i.e., elational liking, peceived elational closeness, and peceived elational similaity) contibuted to its modeating effect. Specifically, we eanalyzed the egession models, each time substituting the composite measue of social elationship quality with one of its dimensions. As Table shows, the inteaction effect was stongest fo the liking dimension (p <.01 fo goup, agent, and patne values) in the compaison of goup and single ecommendes. Although liking was the only dimension that also affected goup value diectly, the modeation was not limited to liking; the goup ecommende social elationship quality inteaction also was significant fo elational closeness and peceived similaity. The esults diffeed in the goup and no ecommende compaison, fo which none of the dimensions was significant when studied in isolation. Thus, without the compomise effect of single ecommendes, we conclude it is the combination of the social elationship quality dimensions, athe than its individual dimensions, that accounts fo the modeation effect. Discussion and Implications Key Intellectual Insights This eseach pesents the fist empiical investigation of the powe of automated goup ecommendes. In two laboatoy expeiments in which consume dyads actually watched movies they had selected in diffeent conditions (goup, single, and no ecommende), we found that goup ecommendes povided substantially e goup value than standad single ecommendes when agents had to fol a ecommende s selections. Although this effect disappeaed when agents could choose feely among a set of movies (i.e., with noise), goup ecommendes still outpefomed single ecommendes when the social elationship quality between the agent and othe goup membes was. value was not e fo goup ecommendes despite the compomise equied fom the agent; the effect was evidently compensated fo by the gatification of pleasing a viewing patne. ecommendes also geneated e goup value than a constellation in which the agent lacked access to a ecommende system, assuming the agent held a positive attitude towad the use of automated ecommende systems in the futue. Manageial Implications Ou findings have substantial manageial elevance, consideing that vitually evey majo commecial ecommende focuses on the geneation of ecommendations fo individual consumes, without addessing the pominent ole of goup consumption in hedonic settings. Ou findings povide evidence that goup ecommendes can incease goup value; theefoe, etailes and ecommende sites should conside offeing them. Thei effectiveness is paticulaly fo cetain constellations (e.g., social elationship quality, agents with positive attitudes towad the use of ecommendes), and companies should pomote such sevices among these segments in paticula. A majo question fo ecommende povides is be how to offe value-maximizing infomation fo each consume segment. Figue povides a decision tee model that can guide manages in offeing (goup) ecommendes, while accounting fo contextual factos. Fo consumes who value ecommendes in geneal, distinguishing between goup and individual consumption situations is cucial; such infomation might be collected with a Plan to watch with othes? button. t all goup membes will be uses of the system, an issue that lights the potential lack of availability of use pefeence infomation. Offeing goup membes who ae not egisteed uses of the system an easy means to eveal thei pefeences would incease the numbe of situations in which goup ecommendes could be applied. Ou findings also imply the need to conside social elationship quality when offeing ecommendations fo goup consumption. Uses might be asked to povide that infomation, o it could be estimated using infomation stoed in a database. s with social elationship quality (and those that do not povide sufficient infomation about social elationship quality) should eceive the standad goup ecommende used heein (o an advanced vesion); those with e social elationship quality instead might access a ecommende that povides pedictions fo all goup membes sepaately and anks ecommended movies accoding to the agent s pefeences, without an aggegated goup value pediction (the altenative goup ecommende in Figue ). Reseach Implications and Limitations Ou eseach daws a fine-gained pictue of goup ecommendes effects on consumes who jointly consume a hedonic poduct. Among the most impotant insights is the cucial ole of a goup s social elationship quality, which modeates the effectiveness of goup ecommendes. ecommendes povide paticulaly goup value to goups chaacteized by a level of social elationship quality. Although the concept of social elationship quality has been studied closely in social psychology, it aely has Automated Systems / 103
104 / Jounal of Maketing, Septembe 2012 TABLE Regession Results fo Social Relationship Quality Dimensions Vesus Single Vesus X = Liking X = Closeness X = Similaity X = Liking X = Closeness X = Similaity Regesso GV AV PV GV AV PV GV AV PV GV AV PV GV AV PV GV AV PV Constant 1.99 2.00 1.241.09.09 4.00.43..10.0.31.9 4.0 4.14 3.1.031 4.1.292 GR.0.042.191.10.019.321.133.012.24.093.241.04.01.19.13.10.20.04 X.0**.01**.**.230.093.3*.144.1.130 1.02**.9**1.09**.30.11.431*.121.11.090 X GR 1.40**1.44**1.31**.09*.30*.*.90*.90*.43.41.9.403.40.324.1.33.4.19 IN N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A..09.19.004.11.22.01.220.29*.144 IN GR N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A..*.410.99*.21*.2.1*.4*.24.1* R 2.23.213.14.092.03.111.0.0.04.29.2.24.1.129.1.144.13.114 R 2 adjusted.20.14.1.09.01.09.030.031.010.24.23.194.101.00.10.0.09.04 *p <.0. **p <.01. tes: All values ae unstandadized egession coefficients. N.A. = not available. GV = goup value, AV = agent value, PV = patne value, GR = goup ecommende, and IN = intention towad ecommende usage.
FIGURE Decision Tee Model fo Systems to use ecommende? ecommende Yes consumption? Single ecommende Yes Name goup membes and detemine social elationship quality t all goup membes in database Add missing goup membes to database and detemine social elationship quality All goup membes in database Yes Social elationship quality High ecommende (focus on a compomise) Low Altenative goup ecommende (focus on agent value) been consideed in maketing eseach that aims to undestand goup consumption decisions. Futhemoe, limited eseach has investigated the extent to which automated ecommendes incease consumes decision making in geneal (Steckel et al. 200). Ou findings contibute to this exciting field by poviding a compaison of goup ecommendes with single ecommendes and with no ecommende infomation. The goup ecommende we used in this study did not impove decisions in all conditions; the same finding applied to the single ecommende. Instead, we identified cetain conditions in which goup ecommendes contibute value. In addition to goups with social elationship quality, agents with positive attitudes towad automated ecommendes (and the goups to which they belong) benefit significantly fom thei use. Although the somewhat limited effect of ecommendes might seem supising, it is consistent with the findings of the only othe study that has compaed ecommende-based decisions with human decisions (Kishnan et al. 200). To ule out potential confounding effects due to the specific ecommende system we used, we pefomed a seies of compaison tests with ecommende algoithms that have been established in theoy and pactice (e.g., Adomavicius and Tuzhilin 200; Das et al. 200; Jannach et al. 2011). Specifically, we compaed the pediction accuacy of ou algoithm with two use-to-use k-neaest neighbo (knn) collaboative filteing algoithms, two item-to-item knn collaboative filteing algoithms (k = 0), and two matix factoization appoaches (f = 300) (Funk 200; Koen, Bell, and Volinsky 2009) using appoximately 1.1 million atings. As we detail in Appendix B, this pefomance compaison showed that although most othe algoithms exhibit somewhat e pediction accuacy, the diffeences wee small. Because ou ecommende is attactive in pactice (i.e., the site is one of the top five Geman movie sites) and the altenative algoithms wee optimized accoding to ou spe- Automated Systems / 10
cific data set (wheeas the oiginal algoithm was used as is, without ecalibations to fit the sample), we conclude that the algoithm we used poduces easonable pedictions that ae compaable to state-of-the-at ecommende techniques. Thus, ou study esults should not be attibuted to the specific ecommende used o the quality of its ecommendations. Nevetheless, because algoithms and inteface designs of ecommendes invaiably influence thei effectiveness (Flede and Hosanaga 2009; Helocke et al. 2004), additional eseach should test othe algoithms and intefaces. In addition, vaiations based on goup decisionmaking eseach might featue altenatives to the mean value aggegation of goup membe pefeences that we used. We study the use of ecommendes in a specific context; consumes had to choose fom a limited numbe of altenatives (i.e., 24 movies). Ou intentional exclusion of blockbuste titles might have inceased pediction eo because consume peceptions of nonmainsteam movies likely ae moe dispesed. Futhe eseach should test whethe the esults diffe fo othe kinds of films and, moe geneally, fo othe poducts. Ou model assumes that the ecommende can geneate ecommendations fo each goup membe. The decision tee model in Figue lights that this assumption is a limitation because, in pactice, such availability is fa fom guaanteed (Aibag, Aoa, and Kang 2010). Appoaches to ovecome this limitation might include (1) designing the pefeence geneation pocess to offe usability, paticulaly fo consumes who ae not fans of the poduct but have athe limited involvement in the categoy; (2) imputing pefeence data povided by the patne on othe sites and netwoks, such as Facebook, though this appoach, in addition to pesenting technical issues, aises concens about pivacy and patnes willingness to povide access to thei data on othe sites; and (3) asking agents to povide the equied pefeence infomation about thei patnes by answeing questions about thei pefeences fo altenative poducts, which the system then would use to poxy fo infomation diectly povided by patnes. The validity of such appoaches emains unclea, and moe eseach is needed to shed light on this impotant issue. Finally, we limit ou empiical design to goups of two consumes (i.e., dyads), consistent with pevious eseach on goups and the dominance of such goups in seveal hedonic industies. Howeve, the complexity of decision making and pefeence modeling inceases exponentially fo lage goups, so it would be valuable to discove the extent to which the esults hold fo lage goups. Constuct Reactance Liking Similaity Closeness APPENDIX A Measues Scale items 1. I chose this movie because I wanted to. 2. I do not feel foced duing the movie choice. 3. I become angy when my feedom of choice is esticted to thee movies. 4. I become fustated when I am unable to make fee and independent movie decisions. 1. I like my movie-patne vey much as a peson. 2. I think my movie-patne is a good fiend. 3. I get along well with my movie-patne. 1. My movie-patne and I ae simila in tems of ou outlook, pespective, and values. 2. My movie-patne and I see things in much the same way. 3. My movie-patne and I ae alike in a numbe of aeas. 1. My movie-patne and I ae in a vey close elationship. 2. Please cicle the pictue be which best descibes you elationship. Conbach s a Expeiment 1/2.33/N.A. Adapted fom Deci et al. 1994; Hong and Faedda 199; Unge and Kenan 193.34/. Liden, Wayne, and Stilwell 1993; Wayne and Feis 1990.909/.913 Liden, Wayne, and Stilwell 1993; Wayne and Feis 1990.19/.42 Aon, Aon, and Smollan 1992 to use ecommende systems 1. Using automated ecommende systems egulaly could be vey helpful. N.A./N.A. Maheswaan and Meyes-Levy 1990 tes: N.A. = not applicable. 10 / Jounal of Maketing, Septembe 2012
Appendix B: Compaison of Pedication Accuacy with Othe Algoithms A subset of the data employed in ou study povided the input fo the compaison tests. Of all uses of the ecommende site, we compaed uses who had at least 12 atings left fo taining of the pediction model, afte withholding the latest atings fo validation puposes. Theefoe, the taining set consisted of 1,140, atings fom,93 uses given to 12,24 movies. Fom each use s pofile, we withheld the latest atings to fom a holdout set, which we used to compae the pediction accuacy of the diffeent algoithms. The holdout set used to calculate measues of pediction accuacy contained 4,10 atings fom the same uses on,03 movies. Table B1 summaizes the esults. TABLE B1 Algoithm Pefomance Compaison Mean Compaed Compaed Absolute with Study with Study Algoithm Type Eo Algoithm RMSE Algoithm Algoithm used Collaboative filteing 1.3 22. (use-to-use, Euclidian distance) Use-to-use, Peason Collaboative filteing 1.921 2.% 22.1 3.0% Use-to-use, cosine Collaboative filteing 1.33.0% 22.1 1.3% Item-to-item, Peason Collaboative filteing 1.0 3.3% 22.14 3.0% Item-to-item, cosine Collaboative filteing 1.214.9% 22.21 1.% Funk (200) Matix factoization 1.91 2.4% 22.139 3.1% Koen, Bell, and Volinsky (2009) Matix factoization 1.9 3.% 21.111.% Aveage deviation 2.1% 3.2% REFERENCES Adamowicz, Wikto, Michel Hanemann, Joffe Swait, Reed Johnson, David Layton, Michel Regenwette, et al. (200), Decision Stategy and Stuctue in Households: A s Pespective, Maketing Lettes, 1 (3/4), 3 99. Adomavicius, Gediminas and Alexande Tuzhilin (200), Towad the Next Geneation of Systems: A Suvey of the State-of-the-At and Possible Extensions, IEEE Tansactions on Knowledge and Data Engineeing, 1 (), 34 49. Aiken, Leona S. and Stephen G. West (1991), Multiple Regession: Testing and Intepeting Inteactions. Newbuy Pak, CA: Sage Publications. Ansai, Asim, Skande Essegaie, and Rajeev Kohli (2000), Intenet Recommendation Systems, Jounal of Maketing Reseach, 3 (August), 33. Aibag, Anocha, Neeaj Aoa, and Moon Young Kang (2010), Pedicting Joint Choice Using Individual Data, Maketing Science, 29 (1), 139. Aon, Athu, Elaine N. Aon, and Danny Smollan (1992), Inclusion of Othe in the Self Scale and the Stuctue of Intepesonal Closeness, Jounal of Pesonality and Social Psychology, 3 (4), 9 12. Aoa, Neeaj and Geg M. Allenby (1999), Measuing the Influence of Individual Pefeence Stuctues in Decision Making, Jounal of Maketing Reseach, 3 (vembe), 4. Bagozzi, Richad P. (2000), On the Concept of al Social Action in Consume Behavio, Jounal of Consume Reseach, 2 (3), 3 9. Baie, Daniel and Eva Stübe (2010), Acceptance of Recommendations to Buy in Online Retailing, Jounal of Retailing and Consume Sevices, 1 (3), 13 0. Bodapati, Anand V. (200), Recommendation Systems with Puchase Data, Jounal of Maketing Reseach, 4 (Febuay), 93. Bohlmann, Jonathan D., Jose Antonio Rosa, Ruth N. Bolton, and William J. Qualls (200), The Effect of Inteactions on Satisfaction Judgments: Satisfaction Escalation, Maketing Science, 2 (4), 301 321. Bottomley, Paul A. and Stephen J.S. Holden (2001), Do We Really Know How Consumes Evaluate Band Extensions? Empiical Genealizations Based on Seconday Analysis of Eight Studies, Jounal of Maketing Reseach, 3 (vembe), 494 00. Behm, Jack W. (19), A Theoy of Psychological Reactance. New Yok: Academic Pess. Chao, Dennis L., Justin Balthop, and Stephanie Foest (200), Adaptive Radio: Achieving Consensus Using Negative Pefeences, in Poceedings of the 200 Intenational ACM SIG- GROUP Confeence on Suppoting Wok. New Yok: Association fo Computing Machiney, 120 23. Chung, Tuck S., Roland T. Rust, and Michel Wedel (2009), My Mobile Music: An Adaptive Pesonalization System fo Digital Audio Playes, Maketing Science, 2 (1), 2. Chuchill, Gilbet A., J. (199), Paadigm of fo Developing Constucts, Jounal of Maketing Reseach, 1 (Febuay), 4 3. Collins, Nancy L. and Stephen J. Read (1990), Adult Attachment, Woking Models, and Relationship Quality in Dating Couples, Jounal of Pesonality and Social Psychology, (4), 44 3. Das, Abhinadan S., Mayu Data, Ashutosh Gag, and Shuyam Rajaam (200), Google News Pesonalization: Scalable Online Collaboative Filteing, in Poceedings of the 1th Intenational Confeence on Wold Wide Web. New Yok: Association fo Computing Machiney, 21 0. Deci, Edwad L., Haleh Eghai, Bian C. Patick, and Dean Leone (1994), Facilitating Intenalization: The Self-Detemination Theoy Pespective, Jounal of Pesonality, 2 (1), 119 42. De Vany, Athu and W. David Walls (1999), Uncetainty in the Movie Industy: Does Sta Powe Reduce the Teo of the Box Office, Jounal of Cultual Economics, 23 (4), 2 31. Epp, Ambe M. and Linda L. Pice (200), Family Identity: A Famewok of Identity Inteplay in Consumption Pactices, Jounal of Consume Reseach, 3 (1), 0 0. FFA (2011), De Kinobesuche 2010, (Apil), (accessed May 11, 2011), [available at http://ffa.de/stat/download.php?file= publikationen/ kinobesuche_2010.pdf]. Automated Systems / 10
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