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104 Received March 2008 Revised November 2008 Accepted July 2009
The role of mixed emotions in consumer behaviour Investigating ambivalence in consumers’ experiences of approach-avoidance conflicts in online and offline settings Elfriede Penz Wirtschaftsuniversita¨t (WU), Institute for International Marketing and Management, Vienna, Austria, and
Margaret K. Hogg Department of Marketing, Management School, Lancaster University, Lancaster, UK Abstract Purpose – Mixed emotions (i.e. consumer ambivalence) play a central role in approach-avoidance conflicts in retailing. In order to assess how consumer ambivalence impacts shopping behaviour, this paper seeks to conceptualize and investigate the multi-dimensional antecedents of approach-avoidance conflicts, experienced by shoppers in changing retail environments, and the importance of approach-avoidance conflicts for consumers’ decision to stay and complete their purchase in that particular shopping channel. Design/methodology/approach – Using a cross-country study, which compared online and offline consumers, the paper tested the influence of the situation, product, and reference group on shoppers’ intentions; and identified how consumers’ mixed emotions influenced approach-avoidance conflicts in different retail settings. Findings – Whereas some distinctions could be drawn between online and offline contexts when examining the impact of market-related, product-related and social factors on consumers’ decision to shop (H1, H2, H3 and H4), no clear distinction could be drawn between online and offline channels in terms of mediating effects of mixed emotions (H5, H6 and H7). Mixed emotions (ambivalence) did mediate the impact of certain product-related, market-related and personal factors on consumers’ intention to purchase. Practical implications – Retailers need to reduce the impact of consumers’ emotional responses to the retail setting where mixed emotions are likely to lead to consumers leaving the stores. For online shops, those retailers are successful who are able to induce behavioural reactions that make consumers return and explore the web site and not use it for search only. Originality/value – Responding to calls for further research on mixed emotions and their consequences, the paper captures the complex impact of consumers’ mixed emotions on approach-avoidance conflicts, and thereby extends earlier work on consumer ambivalence. Keywords Internet shopping, Consumer behaviour, Retailing Paper type Research paper European Journal of Marketing Vol. 45 No. 1/2, 2011 pp. 104-132 q Emerald Group Publishing Limited 0309-0566 DOI 10.1108/03090561111095612
The authors would like to thank Christina Wastlbauer and Christina-Isidora Kyritsi who undertook the data collection for this study. The authors would also like to thank the Editors and the three reviewers for their helpful and insightful comments on earlier drafts of this paper.
Introduction Emotions are central to consumption (Bagozzi et al., 1999; Holbrook and Hirschman, 1982; Leone et al., 2005; Watson and Spence, 2007) and particularly to understanding consumer behaviour in service environments such as retailing (Foxall and Greenley, 1999). Mixed emotions (i.e. consumer ambivalence) play a key role in consumer behaviour. However, with a few exceptions (e.g. Otnes et al., 1997), current research has largely failed to capture the complex impact of consumers’ mixed emotions on approach-avoidance conflicts. In response to the specific call for further research into the “ramifications of mixed emotions” (Watson and Spence, 2007, p. 506), and in order to contribute to earlier work on consumer ambivalence (Olsen et al., 2005; Otnes et al., 1997; Ruth et al., 2002), we conceptualize and investigate the inter-relationship between consumer ambivalence (i.e. mixed emotions) and the multi-dimensional antecedents of approach-avoidance conflicts in different retail settings. Retail channels represent a rich empirical context for investigating approach-avoidance conflicts in consumer behaviour (Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005). While there is increasing research on e-channels as new distribution modes (Wikstro¨m, 2005) and also on the emotional aspects of consumer choices in online environments (Eroglu et al., 2003; Fiore et al., 2000, 2005; Menon and Kahn, 2002), very little is known about the different motivational conflicts that occur in online compared with offline shopping environments. Our aim is to investigate how online shopping and traditional brick and mortar shopping environments differ with respect to the inter-relationship between consumer ambivalence and approach-avoidance conflicts. Our objective is to examine the antecedents that cause consumers to use one retail channel as a way of escaping from the other channel in response to the negative or mixed emotional states provoked by their patronage of the original channel. This will contribute to understanding why consumers abandon their purchases in a brick and mortar store in favour of an online channel; or, alternatively, quit online transactions and turn to offline channels. The findings will contribute to the development of managerial strategies, which could help reduce these lost sales. We begin by reviewing current research on mixed emotions, ambivalence and approach-avoidance conflicts. We then consider the impact of antecedents (linked to product-relevant, market-relevant, Pan and Zinkhan, 2006) and social factors such as the situation, the product or service, and social influences on purchase intentions in online and traditional brick and mortar shopping environments. From this literature review we derive a conceptualization, which models ambivalence in relation to approach-avoidance conflicts. Hypotheses are formulated and tested using a large-scale international survey. We present and interpret our findings; and conclude with a more general discussion first, about how the impact of various antecedents of approach-avoidance conflicts differs across retailing channels; and second about the mediating effect of ambivalence (i.e. mixed emotions) on consumers’ experiences of approach-avoidance conflicts, and their intentions to purchase. Conceptual background Consumer ambivalence and approach-avoidance conflicts in online and traditional brick and mortar shopping environments A consumer’s intention to buy a product does not necessarily result in its purchase. Consumers may have a strong desire to buy a product. However they can feel
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constrained by various factors from completing a purchase, and can sometimes even entirely abandon their initial intention to purchase. Purchase decisions may be abandoned because of a “competition between incompatible responses” within an individual (Miller, 1944, p. 431). The simultaneous appearance of positive and negative tendencies leads to an inner conflict and results in approach or avoidance behaviour. The differences between approach and avoidance motivation have been linked to valence: “in approach motivation, behavior is instigated or directed by a positive/desirable event or possibility, whereas in avoidance motivation, behavior is instigated or directed by a negative/undesirable event or possibility” (Elliot and Thrash, 2002, p. 804). Approach-avoidance conflict theory therefore helps us understand how consumers think about and manage the potential negative aspects or consequences (avoidance factors) involved in making a purchase decision (for more on approach factors, see also Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005). Consumers are motivated to manage the mixed emotions they experience in the marketplace that either drive them towards or inhibit them from making purchases either online or offline. Overload is one of the key antecedents of consumer ambivalence where consumers feel “overwhelmed or ill-prepared during the purchasing process and the sheer volume of purchasing decisions to be made” (Otnes et al., 1997, p. 87). The freedom to choose turns into a form of hyperchoice, i.e. consumers are confronted with (and are often overwhelmed by) the paralyzing nature of choice represented by the increasing number of outlets to choose from; by having to decide what to choose from an ever-expanding range of products and services; and also deciding when to make their choice. Greater choice is thus accompanied by fears of not being able to control one’s behaviour; the risk of information overload; and cognitive dissonance evoked by feelings of regret which are derived, in part, from the products which have not been selected (Harrison et al., 2006). Individuals are generally motivated to minimize their experiences of arousal generated via such tensions, inconsistencies, or mixed emotions. However a certain level of arousal has been shown to be indispensable for evoking emotions such as pleasure, joy or happiness, and also their negative opposites (e.g. Priester and Petty, 1996; Schachter and Singer, 1962). On experiencing arousal, individuals are motivated to search for possible causes for their state of arousal in the environment (Schachter and Singer, 1962). This allows individuals to label the arousal and the positive emotions they experience, such as joy or pleasure. Unexplained arousal, in contrast, is perceived as unpleasant and usually leads to the assignment of a negative label, e.g. fear or apprehension (Hogg and Vaughan, 2005). In early work on choice, Hansen (1976) modelled an inverted u-shaped conflict curve that resulted from the stimulation of the environment, and captured the divergent influence of arousal on individual experience. The conflict between arousal and the environment is seen to be at its highest at a moderate level of arousal. This is noteworthy since Donovan and Rossiter (1982) argue that arousal has positive effects only in pleasant environments while an unpleasant store, for example, might even lead to negative emotions. This is true only for positive (pleasant) or negative (unpleasant) situations. In a neutral situation (neither pleasant nor unpleasant) moderate arousal leads to greater approach, while very high or very low arousal leads to avoidance behaviours. Figure 1 illustrates a possible link
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Figure 1. Possible linkage between ambivalence and approach-avoidance behaviour in conflicts
between ambivalence (mixed emotions) and approach-avoidance conflicts based on the combination of pleasure and arousal. Emotional reactions to any environment include pleasure-displeasure (PL), arousal-nonarousal (AR) and dominance-submissiveness (DO) (known as PAD dimensions) and are said to fully account for emotional responses to any environment (Donovan and Rossiter, 1982; Donovan et al., 1994). PL describes feelings of joy and happiness while AR refers to excitement, alertness and stimulation (Van Kenhove and Desrumaux, 1997, p. 353). Emotions can be viewed as the outcome of explaining arousal, and are positive, negative or mixed (ambivalence) (Harrist, 2006; Hogg and Vaughan, 2005; Schachter and Singer, 1962). Baker et al. (1992) argued that “affective states produced by the store environment do influence consumers’ willingness to buy” (p. 457). Affective states determine whether or not consumers spend time in a store and will return again (Donovan and Rossiter, 1982). The conflicting nature of reactions, shapes the perception of ambivalence, and consumers’ consequent experiences of approach, and avoidance tendencies. Within the framework of human-computer interaction, affect and emotion have been identified as important influences (see Peter and Beale, 2008). With regard to online shopping, the emotional states of pleasure and arousal are important for their potential impact on consumers’ future behaviours, particularly browsing (Menon and Kahn, 2002). Pleasure experienced during shopping online leads to positive attitudes towards future approach behaviour and a preference for stimulation. Stimulation, in turn, affects future approach behaviour because too much arousal due to over-stimulating environments can lead to lower browsing behaviour. From here, we propose the following hypotheses regarding the emotional states and behavioral aspects of approach-avoidance conflicts H1. Emotional states (pleasure, H1a; arousal, H1b; dominance, H1c), behavioural aspects of approach-avoidance conflicts (enjoyment, H1d; return and explore, H1e) as well as ambivalence (H1f ) differ between offline and online stores.
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In investigating ambivalence, most studies of approach-avoidance behaviour have concentrated on consumers’ experiences within the retail environments, rather than examining how shoppers are influenced in their choices by the variety of conflicts that they potentially experience in relation to other market-related, product-related (Pan and Zinkhan, 2006) and social factors. These factors can include the situation (e.g. the rate of information in the store environment); the product or service to be purchased (e.g. perceived product risk; level of involvement); and the social influences such as reference group. These all influence intentions to patronize or purchase from a store; and often generate approach-avoidance conflicts linked to well-recognized emotional states such as arousal, pleasure, enjoyment and the desire to explore the store in the retail environment. The next section discusses possible sources of approach-avoidance conflict. Antecedents to approach-avoidance conflicts Environmental psychology is drawn on in order to gain a thorough understanding of the consumers’ internal processes and behavioural responses to the two different retail store formats. It informs our understanding of the: (1) Impact of environmental stimuli on consumers, as well as of the influences from the social environment. (2) The influence of the merchandise itself. Physical environment – environmental stimuli Retail atmospherics are important for both offline and online channels because they comprise variables that affect shopping behaviour (Donovan and Rossiter, 1982, p. 34). Within retailing management, the influence of the store atmosphere on consumer responses is conceptualized in earlier studies (e.g. Donovan and Rossiter, 1982; Donovan et al., 1994), drawing heavily on the Mehrabian-Russell environmental psychology model (see Foxall and Greenley, 1999, for a detailed review of earlier studies, which have employed this model; Mehrabian and Russell, 1974). Store atmosphere is seen as a better predictor of consumers’ patronage of a store because it overcomes previous limitations of using unidimensionality and singular variables when exploring the concept of store image (Donovan and Rossiter, 1982; Donovan et al., 1994). In addition, the behavioural perspective model (Foxall, 1997; Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005) outlines the influence of the intersection between the consumer behaviour setting and learning history on consumer behaviour. Among store atmosphere variables, the information rate is the main construct that accounts for the influence of environmental stimuli on consumer responses (Mehrabian and Russell, 1974). Information rate represents the degree of information inputs in a channel during a certain time frame (Spies et al., 1997), and comprises novelty, complexity, density and size (Donovan et al., 1994; Tai and Fung, 1997). While novelty refers to unexpected, surprising and new elements, complexity refers to the number of elements and changes in the environment. Density describes those elements in the environment that either made a person feel that they have sufficient privacy within which to shop; or make a person feel crowded, threatened and restricted in terms of movement. Finally, size refers to the scale of the store, the interior decoration, or spatial
elements such as how well the merchandise is organized and displayed (Tai and Fung, 1997). The physical environment can be classified into two sets of factors: ambient (smell, temperature, music) and design (location of items, size of store) (Baker, 1986; d’Astous, 2000). These factors can be the source of shopping irritants (d’Astous, 2000) inducing negative emotions and behaviour in the retail channel. (Note that irritants can also stem from social factors, such as crowding or the behaviour of the sales personnel; and also from characteristics of the product itself, e.g. unfavourable information; Mizerski (1982)). Ambient factors are not noticed unless they reach an unpleasant level, e.g. when the lighting is too bright or the music is too loud. Design factors, however, are actively evaluated by shoppers and include aesthetic elements such as architecture, colour and style, which can increase a consumer’s sense of pleasure when entering a store. Functional design elements, such as layout or comfort, can also contribute to a shopper’s sense of well being. Atmospheric effects have been shown to influence online purchase intentions, for example music (Allan, 2008; Wu et al., 2008), and colours (Wu et al., 2008). Just as consumers are influenced by the rate and amount of information in store (e.g. its novelty, complexity, density and size, Spies et al., 1997), which can stimulate or overwhelm customers, there is similar evidence for consumers’ experiences of feeling stimulated or overwhelmed by web sites when shopping online (Fiore et al., 2005). For online stores, web site design and navigation are vital, and research has confirmed the importance of clear interfaces during all stages of the decision making process (Palmer, 2002; Sinkovics and Penz, 2005). The design of the web site is central to determining the atmosphere experienced by consumers online (McCarthy and Aronson, 2000; Wang and Tang, 2003). In a model of consumers’ web navigational behaviour a series of influencing variables were identified, which included flow experience, need for cognition or level of preferred stimulation, as well as situational factors such as site involvement (Richard and Chandra, 2005). The retail atmospherics experienced in an online store can lead to positive emotions, and thus to favourable attitudes towards online-shopping, the presented goods, and consequently higher satisfaction with a purchase (Eroglu et al., 2001; Eroglu et al., 2003). Online shopping usually wins in terms of price advantage and convenience when compared to traditional retail formats, but the online channel is less attractive with regard to enjoyment and sociability. The social aspects of shopping have been a long-standing source of consumer pleasure in offline retail environments [see the following] (e.g. Dennis et al., 2002; Fiore et al., 2005; Tractinsky and Rao, 2001). We propose that the store’s physical attributes, i.e. the information rate, differ between offline and online shopping environments. In more detail we hypothesize that: H2. Online stores are perceived as more complex (H2a), more novel (H2b) and denser (H2c) respectively than offline stores. Physical environment – social stimuli and reference group influence The physical environment also includes social factors (Baker, 1986), which refer to the presence of an audience, fellow customers and/or service personnel who are responsible for generating the atmosphere (which can be positive, e.g. a lively bar; or negative, e.g. busy checkout queues). The attractive appearance and the pleasant behaviour of service personnel, as well as the presence of other customers, can also be
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reassuring components in a retail environment. Other social factors include reference groups, which are “social groups that are important to a consumer and against which he or she compares himself or herself” (Escalas and Bettman, 2003, p. 341). The store environment, such as crowding and stimulation, as well as sales staff assistance, can increase impulse purchases (Mattila and Wirtz, 2008). Reference group influence depends on the type of product (e.g. luxury or necessity) and whether the products are consumed publicly or privately (Childers and Rao, 1992). Reference groups exert two main types of social influence: informational and normative (the latter can be either value-expressive or utilitarian) (Bearden and Etzel, 1982; Childers and Rao, 1992; Mangleburg et al., 2004). Word of mouth in this shopping context can be linked to mavenism, retail opinion sharing, and susceptibility to interpersonal influence. Susceptibility relates either to shoppers’ needs to receive rewards or avoid being punished (i.e. the exertion of utilitarian influence), or to shoppers’ needs for “psychological affiliation” (i.e. value-expressive influence) (Childers and Rao, 1992, p. 199). While reference groups play a very important role in traditional brick and mortar stores, very little is known about the effects of this type of social influence on consumers when shopping online. Except for participating in virtual communities or chat groups, online shoppers act in a “lonely crowd” (Burton, 2002, p. 793). Some consumers might want to use online shopping on purpose to avoid consumer culture, choosing to deliberately avoid the sociability associated with traditional retail outlets. This provides evidence for what might be seen as the anti-social nature of some of these forms of online shopping (Burton, 2002). We propose that the reference group’s influence differs between online and offline shopping environments. Thus, the following is hypothesized: H3. Normative influence (H3a), informational influence (H3b), interpersonal influence (H3c) and opinion sharing on decision-making (H3d ) respectively are stronger in an offline than in an online store. Product influence Goods or services involve different levels of perceived risk and involvement. The concept of risk encompasses both potentially positive and negative outcomes (Mitchell, 1999), with a focus mainly on potentially negative outcomes. Perceived risk comprises “subjective expectations of loss” (Stone and Gronhaug, 1993, p. 42) linked to uncertainty and consequences (Conchar et al., 2004; Cox, 1967; Dowling and Staelin, 1994; Hansen, 1976; Jacoby and Kaplan, 1972; Kaplan et al., 1974; Mitchell and Hogg, 1997). Uncertainty involves confidence, reliability, dependability, trust likelihood and probability, whereas consequences involve trust, danger, relevance and seriousness (Mitchell and Hogg, 1997). Perceived risk encompasses several dimensions such as product performance, financial, psychological, time, social, and physical risk (Kaplan et al., 1974). Risk perception increases rather than decreases in different phases of the consumer decision-making process (Mitchell and Boustani, 1994): pre-purchase search and purchase evaluation can be very challenging phases. Involvement, on the other hand, is the importance which a shopper attaches to a particular product “based on their inherent needs, values and interests” (Zaichkowsky, 1985, p. 342). High involvement products are regarded as more risky than low involvement products. With regard to product involvement, research shows that
consumers seeking excitement use the internet for surfing, downloading software or communicating (Schiffman et al., 2003). Consumer purchase behaviour when shopping online is fundamentally different from traditional or offline-shopping (Dennis et al., 2002), indicating that standardized or search goods are usually more suitable for online-shopping (e.g. books, music) when compared with experience goods (e. g. personal care products, clothes, Chiang and Dholakia, 2003; Kwak et al., 2002; Monsuwe et al., 2004; Shim et al., 2001); and supporting the argument that the more tangible a product, the more suitable it is for online-shopping (see also Koernig, 2003, on services). Perceived risk, in its turn, was found to function as a motivational barrier to purchasing online (Mariani and Zappala`, 2006; Penz and Kirchler, 2006) and to have a negative impact on the evaluation of the experience of online-purchasing. This helps to explain barriers to online-shopping (Forsythe and Shi, 2003; Teo and Yeong, 2003). In order to reduce risk, strategies such as using reference group appeal, marketer’s reputation or brand image are proposed (Tan, 1999). Therefore, we propose the following: H4. Product risk (social, H4a; financial, H4b; performance, H4c) is higher in an online than in an offline store, but product involvement (need for product, H4d; exciting product, H4e) is higher in an offline than in an online store. Emotional states and behavioral aspects of approach-avoidance conflict that intervene to affect the predictability of purchase intention So far, we have emphasized the value of including emotional states in explaining consumer responses (H1) and have discussed the importance of three factors (situation, product, reference group) in two retail formats (H2, H3 and H4). Next, therefore, we develop a conceptual model (see Figure 2) that deals with the mediating effect of emotional states and behavioural aspects of approach-avoidance conflicts on the relationship between the conflict antecedents and purchase intentions. Based on the conceptualization as illustrated in Figure 2, the following hypotheses were formulated: H5. The impact of the store’s physical attributes (complexity, H5a; novelty, H5b; density, H5c) on future shopping intentions is mediated by emotional states, behavioural responses and ambivalence. H6. The reference group influence (normative influence, H6a; informational influence, H6b; interpersonal influence, H6c; decision making, H6d ) on future shopping intentions is mediated by emotional states, behavioural responses and ambivalence. H7. The influence of product characteristics (social risk, H7a; financial risk, H7b; performance risk, H7c; need for product, H7d; exciting product, H7e) on future shopping intentions is mediated by emotional states, behavioural responses and ambivalence. See Table I for a summary of the concepts and hypotheses developed for empirical testing.
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Figure 2. Conceptual model of approach-avoidance behaviour in conflicts and related hypotheses
Table I. Overview of concepts and developed hypotheses
Situation Information rate Complexity Novelty Density Reference group Interpersonal influence susceptibility Normative influence Informational influence Retail opinion sharing Interpersonal influence Decision making Product Product risk Social risk Financial risk Performance risk Product involvement Need for product Exciting product
Hypotheses Difference between online and offline
Mediated effects on intention
H2a H2b H2c
H5a H5b H5c
H3a H3b
H6a H6b
H3c H3d
H6c H6d
H4a H4b H4c
H7a H7b H7c
H4d H4e
H7d H7e
Research design The purpose of this research was to extend understanding of consumer ambivalence by identifying and investigating the impact of mixed emotions on approach-avoidance conflicts in consumer behaviour using a comparative study of online and offline settings. From an initial literature review and framework, a conceptual model (see Figure 2) was developed of approach-avoidance behaviour in conflicts, and operationalized via a series of related hypotheses, which were tested. An instrument was developed that captured first, the possible sources of conflicts; second, approach-avoidance emotional states; and third, behavioural responses in conflicts. For the questionnaire context, before answering the questions the respondents had to think of a recent shopping situation where they had bought something (either for themselves or someone else) that had led to an approach-avoidance conflict (this was fully explained at the beginning of the questionnaire). Scales were taken from existing literature and were adapted for our research purposes. To capture possible sources of conflicts which derive from: . the purchase/shopping situation, we used Tai and Fung’s (1997) measure of information rate. In addition we measured the effect of store atmosphere (offline only) and web site effectiveness (online only, Sinkovics and Penz, 2005). In order to elaborate the causes related to . products/services, we measured different levels of risk perception (DelVecchio and Smith, 2005; Doolin et al. 2005), and product involvement (Zaichkowsky, 1994). Another set of items measured reference group influence on shopping related aspects. In particular, scales that captured interpersonal influence susceptibility (Bearden et al., 1989, 1990) and retail opinion sharing (Paridon, 2004) were employed. The approach-avoidance conflict was operationalized using Mehrabian and Russell’s (1974) measure of emotional states and the behavioural aspect of intention. Convenience sampling was used for this international study (Austria, Greece, the UK). These countries were chosen first, because consumers in all three countries use both traditional and online modes of shopping; and second because Austria and Greece represent northern and southern Europe respectively, while the UK represents “off shore Europe” with strong links to the USA and its rather different patterns of consumption and shopping channel behaviour. Sampling in international marketing research is critical. In our case we were primarily interested in developing a conceptual model of mixed emotions and the impact on purchase intentions based on an international sample (contextual research) and not on a comparative study that identified similarities and differences across countries (Reynolds et al., 2003). Following Malhotra et al. (1996) we focused on behaviour settings as units for the present study rather than the nation-state information. According to Malhotra et al. (1996), “a behaviour setting represents all the forces acting on individual members of a setting to enter and participate in its operation in particular ways [. . .] The selection of cultures (cultunits or behaviour settings) to be investigated should be based on the theoretical or applied objectives of the study (p. 25)”. Based on extensive exploratory research (which included the collection of stories from respondents in UK and Austria, interviews, secondary data and literature review) in all three countries, we selected shopping (online and brick and mortar) as behaviour settings. Participants were
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approached either in person and invited to complete a hard copy questionnaire; or participants were approached via e-mail with an e-questionnaire that was available on a web site. The condition (offline or online shopping) was randomly assigned to both sets of respondents. In total, there were 335 usable questionnaires (Austria n ¼ 127, Greece n ¼ 111, the UK n ¼ 117). A total of 171 answers referred to traditional shopping contexts (Austria n ¼ 61, Greece n ¼ 53, the UK n ¼ 57), 184 answers were for online shopping (Austria n ¼ 66, Greece n ¼ 58, the UK n ¼ 60). About half of the respondents were aged between 18 and 25 years old. The remaining half was between 26 and 30 þ . A total of 54.1 per cent were female and 45.9 per cent were male. The samples can be characterized as being representative for the younger segments in the respective countries. In the online shopping condition, tickets were the product (f ¼ 76) most often chosen to discuss, followed by electronics (f ¼ 46). In traditional shopping contexts, respondents mainly reported their experiences of purchasing clothing (f ¼ 63) and electronics (f ¼ 52). See Table II for summary statistics of different patterns of online product purchase by consumers in Austria, Greece and the UK compared with consumers overall in the EU 27. Findings In order to test the hypotheses, factor and reliability analyses were first conducted to derive a manageable number of dimensions. The procedure involved running exploratory factor analyses, eliminating those items with double-loadings and/or low loadings and calculating reliability measures (Cronbach’s alpha, Cronbach, 1951) for the remaining items. The explained variance was above 50 per cent for all scales and Cronbach’s alpha ranged from 0.42 to 0.92 (see Table III). The extracted dimensions were the basis for a series of correlation and t-tests and multiple mediated regression analyses. In order to come up with a numerical value for ambivalence, an index was calculated based on subjective ambivalence considerations (Lavine, 2004; Priester and Petty, 1996; Thompson and Zanna, 1995). Our index is based on the assumption that emotional states and behavioural responses could co-exist, either as positive, as
Table II. Percentage of Austrian, Greek and UK individuals (compared to EU 27) purchasing via the internet in the last 12 months (2008)
Travel and holiday accommodation Clothes, sport goods Books, magazines, newspapers Household goods (e.g. furniture, toys, etc.) Tickets Film, music Electronic equipment (including cameras) Computer-software and games Computer-hardware Food, groceries Shares, financial services, insurance Other types of goods or services
Austria
Greece
UK
EU (27)
29 39 44 24 25 20 26 14 16 9 N/A 7
26 19 24 12 14 15 21 22 24 3 1 6
48 42 37 40 37 41 26 22 12 19 11 8
42 41 39 35 33 29 25 21 16 11 9 8
Source: Eurostat (internet purchases by individuals), accessed May 25, 2009
Explained variance (in per cent) Cronbach’s a No. of items Information rate Complexity Novelty Density Product risk Social risk Financial risk Performance risk Product involvement Need for product Exciting product Interpersonal influence susceptibility Normative influence Informational influence Retail opinion sharing Interpersonal influence Decision making Approach avoidance – emotional states Pleasure Arousal Dominance/control Approach avoidance – behavior Enjoyment Return and explore
69.10 0.78 0.75 0.60
3 2 2
0.81 0.81 0.76
5 3 3
0.73 0.77
4 3
0.90 0.79
8 2
0.74 0.71
4 2
0.82 0.67 0.61
6 4 2
0.71 0.42
4 2
65.69
115
62.48 64.59 63.98 59.25
57.76
negative or as a mixture of positive and negative experiences. The index ranges between 2 2.5 and 2.5. The higher the index the more the ambivalence is felt: Ambivalence ¼
Mixed emotions in consumer behaviour
Positive emotions 2 negative emotions 2 2 jPositive emotions 2 negative emotionsj
First of all, we tested H1, H2, H3 and H4 and found significant differences between the retail formats for the effects of various influences (see Table IV). Regarding the emotional states, pleasure (H1a), arousal (H1b) and dominance/control (H1c) were higher in the online store, as well as the desire to return and explore the store (H1e). The felt ambivalence, however, was higher in the traditional store (H1f ). The situation when shopping online is perceived as more complex (confirms H2a). Product-specific influences were found in the performance risk (H4c), which is perceived to be lower in the traditional store. Both forms of product involvement were higher in the traditional store (H4d and H4e). Finally, no differences in terms of reference group influence, was found (rejecting H3a, H3b, H3c and H3d ). In order to find out how the emotional states relate to each other correlation analyses were run separately for the two conditions (online versus offline). In the online condition, arousal was significantly correlated with the other emotional states, i.e. pleasure, dominance, enjoyment of the online store and intention to return and explore the store. Pleasure is also positively related to enjoyment and intention to return and
Table III. Factor and reliability analyses for constructs
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Table IV. Differences between the two different retail formats (online shopping situations vs. traditional brick and mortar “offline” contexts) in terms of the effects of various influences (t-tests, independent samples)
Approach-avoidance conflict Pleasure Arousal Dominance Enjoyment Return and explore Ambivalencea Situation Complexity Novelty Density Product Social risk Financial risk Performance risk Need for product Product exciting Reference group Normative Informational Interpersonal influence Decision making Intention Intention
Online (n ¼ 184) Mean SD
Offline (n ¼ 171) Mean SD
3.63 3.42 3.14 3.04 3.80 20.40
0.72 0.72 0.89 0.90 0.97 1.12
3.33 3.28 2.86 3.12 3.55 20.13
0.74 0.63 0.89 0.86 0.95 1.06
3.88 1.97 3.01 2 0.94 2.44 2 2.34
353 353 353 353 353 353
0.00 * 0.05 * * 0.03 * * 0.35 0.01 * 0.02 * *
3.84 3.18 2.78
1.05 1.01 1.13
3.58 3.08 2.94
0.96 0.90 1.06
2.43 0.96 2 1.34
353 353 353
0.02 * * 0.34 0.18
2.93 2.68 2.66 4.02 3.73
1.00 1.03 1.08 0.73 0.88
3.04 2.67 2.44 3.76 3.51
1.02 1.14 0.97 0.89 0.83
2 1.08 0.11 2.03 2.98 2 2.47
353 353 353 353 353
0.28 0.91 0.04 * * 0.03 * * 0.02 * *
2.15 3.53 3.24 3.98
0.87 0.93 0.74 0.90
2.25 3.49 3.13 3.89
0.88 1.12 0.77 0.79
2 1.06 0.35 1.39 0.99
353 332 353 353
0.29 0.72 0.16 0.32
3.68
0.97
3.53
0.84
1.55
353
0.12
Differences t df
Sig.
a
Notes: 1 = Strongly disagree; 5 = Strongly agree. Ambivalence ranges between 22.5 (low ambivalence) and 2.5 (high ambivalence). *p , 0.01, * *p , 0.05
explore the store. Dominance is positively correlated with pleasure and intention to return and explore the store. Thus, in the online store, high arousal produces positive emotional and behavioural responses. In the traditional store, arousal is not correlated to these emotional states (see Table V). However, enjoyment correlates significantly with pleasure and the willingness to return and explore the store, which is also correlated with pleasure. Finally, pleasure and dominance is also significantly correlated. To sum up, the
Table V. Correlation matrix for approach-avoidance variables (online context)
Arousal Enjoyment Return and explore Pleasure Dominance
Arousal
Enjoyment
Return and explore
Pleasure
Dominance
1.00 0.16 * * 0.22 * 0.31 * 0.24 *
1.00 0.37 * 0.24 * 0.03
1.00 0.23 * 0.26 *
1.00 0.32 *
1.00
Notes: *p , 0.01, * *p , 0.05
emotional states are correlated. While in the online store arousal is linked to the other emotional states, this is not the case in the traditional store. Next, in order to test the mediating effects of emotional states, either as mixed or non-mixed states, a multiple mediation design was used[1]. Mediation exists when a predictor affects a dependent variable indirectly through a mediator, which is an intervening variable. A mediator accounts for a relation between predictor and criterion. It explains how external events take on psychological significance. Mediators specify when certain effects will hold (Baron and Kenny, 1986; Preacher and Hayes, 2004; Preacher et al., 2007). The analytical procedure follows Preacher et al. (Preacher and Hayes, 2004, 2008; Preacher et al., 2007) and makes use of bootstrapping as a method to test for the indirect effect of the mediators on purchase intentions (see Figure 2). In the following the mediating effects of emotional states (pleasure, arousal, dominance), behavioural responses (return and explore, enjoyment) and ambivalence are discussed. Additionally, the context (online versus offline) and gender were included as control variables in the model (see Table VI).
Mixed emotions in consumer behaviour 117
Emotional states mediate the influence of environmental stimuli on purchase intentions Greater complexity significantly leads to greater emotional states (return and explore, enjoyment). In turn, pleasure, return and explore and enjoyment lead to higher purchase intentions. Testing for the mediation effect, the analysis reveals that the total (c) and direct (c’) effects of complexity on purchase intention are 0.42, p , 0:00 and 0.27, p , 0:00, respectively. The difference between the total and the direct effects is 0.15 and a 95 per cent BCa (Bias corrected and accelerated) bootstrap CI (confidence interval) of 0.10 to 0.21 (– 0). Since the CI of the mediators, “return and explore” and “enjoyment” are different from zero they are thus significant mediators (see Table VII). Greater novelty significantly leads to greater emotional states (pleasure, return and explore, enjoyment). In turn, return and explore and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of novelty on purchase intentions are 0.25, p , 0:00 and 0.12, p , 0:01, respectively. The difference between the total and the direct effects is 0.12 and BCa CI95% is 0.06 to 0.19 (– 0). Again, CI of the mediators “return and explore” and “enjoyment” are different from zero and thus represent significant mediators. Greater density significantly leads to greater dominance. In turn, return and explore and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of density on purchase intentions are 2 0.04, p , 0:39 and 2 0.05, p , 0:24, respectively, which means they are not significant. The difference between
Arousal Enjoyment Return and explore Pleasure Dominance
Arousal
Enjoyment
Return and explore
Pleasure
Dominance
1.00 0.00 0.06 0.11 0.05
1.00 0.30 * 0.39 * 0.11
1.00 0.17 * * 0.06
1.00 0.41 *
1.00
Notes: *p , 0.01, * *p , 0.05
Table VI. Correlation matrix for approach-avoidance variables (offline context)
Table VII. Results for multiple mediated regression analyses **
0.12
0.25
0.09 2 0.01 2 0.09 0.25 0.32 2 0.10
Financial risk
1.95 ns 20.21 ns
0.10 2 0.01
0.09 0.00
Coeff 2 0.12 0.08 2 0.12 2 0.04 * * 0.08 ns 0.09
t 1.54 0.25 20.90 0.62 3.60 20.48
p ns ns ns ns
20.32 ns 2 0.09 22.63 * * 2 0.21 R 2 ¼ 0:33; Adj R 2 ¼ 0:31; Fð345; 9Þ ¼ 19:00
5.84
**
Coeff 0.08 0.01 2 0.05 0.03 0.19 2 0.03
2 0.03 2 0.23 R 2 ¼ 0:38; Adj R 2 ¼ 0:36; Fð345; 9Þ ¼ 23:42 Product Social risk
0.27
8.68
* 2.20 20.03 ns 21.63 ns ** 4.59 ** 5.57 21.73 ns
0.11 0.00 2 0.08 0.22 0.27 2 0.08
0.42
t 1.76 0.39 0.35 4.46 6.28 21.31
Coeff 0.09 0.02 0.02 0.23 0.32 2 0.07
Coeff 0.17 0.04 0.05 * * 0.11 * * 0.26 ns 2 0.02
p ns ns ns
Novelty
**
ns
* **
ns ns
p
**
**
1.75 0.05
t 2 2.23 1.58 2 2.25 2 0.78 1.56 1.65
ns ns
ns ns ns
*
ns
*
p
2 0.94 ns 2 2.30 *
2.65
4.83
1.73 ns 2 0.22 ns 2 1.78 ns ** 5.26 ** 6.33 2 2.11 *
t 3.36 0.76 0.98 2.16 5.09 2 0.42
0.11 20.01
Coeff 0.02 20.09 20.06 20.18 0.06 0.03
Performance risk
20.09 20.23 R 2 ¼ 0:32; Adj R 2 ¼ 0:30; Fð345; 9Þ ¼ 18:09
20.05
20.05
0.10 20.01 20.08 0.26 0.34 20.10
Coeff 0.02 0.01 0.13 0.07 20.01 20.01
Density
*
ns ns ns
p ns 0.79
**
ns ns
p ns ns ns
* 2.05 2 0.31 ns
t 0.29 2 1.63 2 1.12 2 3.50 1.17 0.48
2 0.93 ns 2 2.55 * *
2 1.19 ns
2 0.85 ns
* 1.96 2 0.22 ns 2 1.59 ns * 5.32 ** 6.95 2 1.98 *
t 0.32 0.27 2.40 1.33 2 0.11 2 0.14
0.07 2 0.02
Coeff 0.29 0.17 0.04 0.09 0.09 2 0.05
Product need
ns ns ns ns
** **
P
1.35 ns 20.48 ns
t 5.66 3.25 0.75 1.71 1.59 21.01
0.09 2 0.02
Coeff 0.41 0.32 0.18 0.20 0.31 2 0.15
Product excitement
118
IV . mediator variables (emotional states) – a Pleasure Arousal Dominance Return and explore Enjoyment Ambivalence Direct effects of mediator variables (emotional states) . DV (intention) – b Pleasure Arousal
Model fit
Partial effect of control variables on intention Online/offline Gender
Direct effect of IV on DV – c’
Pleasure Arousal Dominance Return and explore Enjoyment Ambivalence Direct effects of mediator variables (emotional states) . DV (intention) – b Pleasure Arousal Dominance Return and explore Enjoyment Ambivalence Total effect of IV on DV – c
IV . mediator variables (emotional states) – a
Store’s physical attributes (situation) Complexity
** ** ** ** ** **
p
1.71 ns 20.38 ns (continued)
t 8.33 6.23 3.41 3.84 5.93 22.71
EJM 45,1/2
Model fit
Partial effect of control variables on intention Online/offline Gender
Direct effect of IV on DV – c’
Pleasure Arousal Dominance Return and explore Enjoyment Ambivalence Direct effects of mediator variables (emotional states) . DV (intention) – b Pleasure Arousal Dominance Return and explore Enjoyment Ambivalence Total effect of IV on DV – c
IV . mediator variables (emotional states) – a
Model fit
Partial effect of control variables on intention Online/offline Gender
Direct effect of IV on DV – c’
Dominance Return and explore Enjoyment Ambivalence Total effect of IV on DV – c *
ns
21.34 ns
2 0.07 0.12
0.19
0.09 2 0.01 2 0.07 0.24 0.34 2 0.10
21.05 ns 2 0.09 22.63 * * 2 0.22 R 2 ¼ 0:33; Adj R 2 ¼ 0:31; Fð345; 9Þ ¼ 19:08
21.34 ns
* 2.00 20.09 ns 21.79 ns ** 4.71 ** 7.11 21.80 ns
0.11 0.00 2 0.09 0.24 0.36 2 0.09
2 0.10 2 0.24 R 2 ¼ 0:32; Adj R 2 ¼ 0:30; Fð345; 9Þ ¼ 18:15
Informational Influence Coeff 0.09 0.01 2 0.12 * * 0.10 * 0.07 * 0.03
p ns ns ns
t 20.29 0.46 21.87 23.94 2.45 2.50
2 0.06
2 0.09
2 0.08
2 0.10 0.25 0.36 2 0.09
Novelty
21.08 ns 2 0.11 22.66 * * 2 0.26 R 2 ¼ 0:32; Adj R 2 ¼ 0; 31; Fð345; 9Þ ¼ 18:47
0.82
2.37
21.68 ns ** 5.28 ** 6.73 22.00 *
Coeff 2 0.02 0.02 2 0.10 2 0.20 0.13 0.13
2 0.10 2 0.24 R 2 ¼ 0:32; Adj R 2 ¼ 0:30; Fð345; 9Þ ¼ 17:97 Reference group Normative Influence
0.04
0.12
2 0.09 0.26 0.34 2 0.10
Store’s physical attributes (situation) Complexity
ns ns
* **
p ns ns
**
**
2 1.03 ns 2 2.45 *
2.74
3.58
1.66 ns 2 0.21 ns 2 1.29 ns ** 5.01 ** 7.03 2 2.07 *
t 1.71 0.14 2 2.23 1.96 1.32 0.56
2 1.15 ns 2 2.84 * *
2 1.94 ns
2 1.46 ns
2 1.87 ns ** 5.14 ** 7.20 2 1.89 ns
20.08 20.23 R 2 ¼ 0:32; Adj R 2 ¼ 0:31; Fð345; 9Þ ¼ 18:52
0.09
0.13
0.10 0.00 20.09 0.26 0.33 20.10
Coeff 0.10 20.07 20.01 20.03 0.11 20.05
Interpersonal Influence
20.12 20.25 R 2 ¼ 0:32; Adj R 2 ¼ 0:30; Fð345; 9Þ ¼ 18:19
20.07
20.08
20.09 0.24 0.35 20.10
Density
** **
*
*
ns
ns ns ns
ns
*
p Ns ns ns ns
2 0.89 ns 2 2.50 *
2.02
2.56
1.81 2 0.03 2 1.67 5.40 6.77 2 1.91
t 1.85 2 1.25 2 0.19 2 0.59 2.04 2 0.96
2 1.24 ns 2 2.74 * *
2 1.42 ns
2 1.55 ns
2 1.79 ns ** 4.86 ** 7.11 2 2.01 *
2 0.09 2 0.21 R 2 ¼ 0:37; Adj R 2 ¼ 0:36; Fð345; 9Þ ¼ 23:03
0.25
0.28
0.08 0.01 2 0.05 0.21 0.37 2 0.06
Coeff 0.08 2 0.02 2 0.07 0.12 2 0.02 2 0.12
Decision making
2 0.08 2 0.23 R 2 ¼ 0:33; Adj R 2 ¼ 0:31; Fð345; 9Þ ¼ 18:59
0.10
0.17
2 0.08 0.25 0.35 2 0.11
*
**
** **
**
**
ns
ns ns ns
*
*
ns
p ns ns ns
20.98 ns 22.39 *
5.63
5.62
1.54 0.20 20.93 4.54 7.77 21.23
t 1.48 20.41 -1.27 2.29 20.31 22.20
20.84 ns 22.50 *
2.12
3.29
21.59 ns ** 5.21 ** 7.06 22.12 *
0.69 21.00 22.53
2 0.09 2 0.23 R 2 ¼ 0:32; Adj R 2 ¼ 0:30; Fð345; 9Þ ¼ 17:94
4.25
21.77 5.22 6.73 22.04
0.04
0.22
2 0.09 0.25 0.34 2 0.10
*
ns
ns
**
** ** *
ns
Mixed emotions in consumer behaviour 119
Table VII.
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the total and the direct effects is 0.01, and BCa CI95% contains zero (2 0.06 to 0.07), thus no mediators contribute to the indirect effect. To sum up, we found evidence for H5a and H5b in that return and explore and enjoyment are aspects of emotional states that significantly mediate the influence of complexity and novelty on purchase intentions. They can reduce the effect of complexity and therefore are important variables to consider. While complexity and novelty lead to stronger return and explore and enjoyment, density impacts the level of felt dominance; in addition, the emotional states have impacts on purchase intentions: return and explore and enjoyment lead to higher, ambivalence to lower, purchase intentions. In terms of recommendations, those stores that make consumers feel they should come back and explore the store further and that lead to feelings of enjoyment are predictors for purchase intentions. In particular, complex and novel environments are responsible for these feelings. Dense stores (packed products, shelf size, etc.) make consumers feel the environment to be controlling and influential. In all models, the difference between online and offline situations was not significant. Emotional states mediate the influence of the reference group on purchase intentions Greater normative influence significantly leads to lower return and explore (p , 0:00) and higher enjoyment and ambivalence (p , 0:05). In turn, pleasure, return and explore and enjoyment lead to higher purchase intention. The total and direct effects of normative influence on purchase intentions are 2 0.07, p , 0:18 and 2 0.06, p , 0:18, i.e. no significant effect of normative influence on purchase intention. The difference between the total and the direct effects is 0.01 and BCa CI95% is not different from zero (2 0.08 to 0.07). CI of enjoyment is 0.01 to 0.10 and thus mediates IV and DV significantly (support for H6a). Greater informational influence significantly leads to lower dominance (p , 0:03) and higher return and explore (p , 0:05). In turn, return and explore and enjoyment, lead to higher, ambivalence to lower, purchase intention. The total and direct effects of informational influence on purchase intentions are 0.19, p , 0:00 and 0.12, p , 0:01. The difference between the total and the direct effects is 0.06 and BCa CI95% is not different from zero (2 0.01 to 0.12). CI of the mediator “return and explore” is different from zero (0.01 to 0.06) and thus mediates IV and DV significantly (support for H6b). Greater interpersonal influence significantly leads to higher enjoyment (p , 0:05). In turn, return and explore and enjoyment, lead to higher purchase intention. The total and direct effects of normative influence on purchase intentions are 0.13, p , 0:01 and 0.09, p , 0:04. The difference between the total and the direct effects is 0.0430 and BCa CI95% is not different from zero (2 0.02 to 0.11). No mediating effects can be found. Greater influence on decision making significantly leads to higher return and explore (p , 0:02) and lower ambivalence (p , 0:03). In turn, return and explore and enjoyment lead to higher purchase intention. The total and direct effects of influence on purchase intentions are 0.28, p , 0:00 and 0.25, p , 0:00. The difference between the total and the direct effects is 0.04 and BCa CI95% is not different from zero (2 0.03 to 0.09). CI of the mediator “return and explore” is different from zero (0.01 to 0.06) and thus mediates IV and DV significantly (support for H6d ). Summarizing the potential mediating effects of the impact of reference group on purchase intention, enjoyment is responsible for the weaker impact of normative
influence on purchase intention, although normative influence is not significant. However, the tendency is that the more consumers enjoy the store, the less they are influenced by others’ behaviour. The willingness to return and explore the store as an emotional reaction to the store lessens the informational influence from others, as well as their impact on the decision-making on purchase intentions. Thus, others are less influential if consumers themselves have the feeling that they should return to the store and explore it further. Emotional states mediate the influence of product aspects on purchase intentions Greater social risk significantly leads to higher enjoyment (p , 0:00). In turn, return and explore and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of social risk on purchase intentions are 0.12, p , 0:01 and 0.04, p , 0:41, respectively, which means that only the simple effect is significant. The difference between the total and the direct effects is 0.01 and BCa CI95% is different from zero (0.02 to 0.15). CI of “enjoyment” is different from zero (0.02 to 0.11) and thus proves to be a significant mediator (support for H7a). Greater financial risk significantly leads to higher pleasure and arousal (p , 0:05). In turn, return and explore and enjoyment, lead to higher purchase intentions. The total and direct effects of financial risk on purchase intentions are 2 0.08, p , 0:14 and 2 0.09, p , 0:05, i.e. no significant effect of financial risk on purchase intention. The difference between the total and the direct effects is 0.01 and BCa CI95% is not different from zero (2 0.06 to 0.07). CI of the mediator “dominance” is different from zero (0.01 to 0.04) and thus proves to be a significant mediator. Although financial risk does not significantly influence dominance, there is a difference between total and direct effect. Greater performance risk significantly leads to lower return and explore (p , 0:00). In turn, pleasure, return and explore, and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of performance risk on purchase intentions are not significant 2 0.08, p , 0:12 and 2 0.06, p , 0:15), as is the difference between the total and the direct effects (2 0.02, BCa CI95% 2 0.09 to 0.05). No CI of mediators is different from zero. Some mediational effects were found with regard to products’ influence on purchase intentions. While enjoyment significantly mediates the influence of social risk, dominance mediates the influence of financial risk on purchase intentions. In other words, in cases where consumers enjoy the situation, the fact that a product signals one’s position to others is less important for the intention to purchase. The influence of financial risk, on the other hand, is mediated by dominance: if the situation is perceived as controlling, the financial risk is less likely to weaken purchase intention. Greater need for product significantly leads to higher pleasure and arousal (p , 0:00). In turn, return and explore, and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of need for product on purchase intentions are 0.17, p , 0:00 and 0.10, p , 0:03, respectively. The difference between the total and the direct effects is 0.07 and BCa CI95% is different from zero (0.01 to 0.15). No CI of mediators is different from zero, thus no mediating effects can be found. Greater excitement for product significantly leads to higher emotional states (p , 0:00). In turn, return and explore, and enjoyment lead to higher, ambivalence to lower, purchase intentions. The total and direct effects of excitement for product on purchase intentions are 0.22, p , 0:00 and 0.04, p , 0:49, respectively. The difference
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between the total and the direct effects is 0.19 and BCa CI95% is different from zero (0.11 to 0.27). CI of return & explore, enjoyment and ambivalence are different from zero (0.03 to 0.09; 0.06 to 0.16; .01 to 0.04), and thus prove to be significant mediators (support for H7e). With regard to product involvement, the excitement for the product is mediated by emotional states: return and explore, enjoyment and ambivalence, weaken the purchase intention. In other words, if the excitement for the product is mixed up with other emotional feelings that refer either to the store (return and explore, enjoyment) or involve different feelings at the same time, the effect of the product’s influence on purchase intentions is weakened. Discussion This study provided more detailed results first, about the impact of a variety of different antecedents on approach-avoidance conflicts, and second about the mediating effect of ambivalence (i.e. mixed emotions) on consumers’ experiences of approach-avoidance conflicts, and their intentions to purchase. Whereas some distinctions could be drawn between online and offline contexts when examining the impact of market-related, product-related (Pan and Zinkhan, 2006) and social factors on consumers’ approach-avoidance conflicts (H1, H2, H3 and H4); no clear distinction could be drawn between online and offline channels in terms of mediating effects of emotions on shoppers’ intention to purchase (H6, H7, H8 and H9). However mixed emotions (ambivalence) did mediate the impact of certain product-related, market-related and personal factors on consumers’ intention to purchase across both channels. For approach-avoidance conflicts, these results indicate much more clearly than earlier research the moderating role-played by different antecedents. The influences of market-related, product-related (Pan and Zinkhan, 2006) and social factors were elicited on consumers’ intention to purchase. Examining the characteristics of the environment, the product, and the reference group, meant that this study extended earlier work, which had concentrated only on the environment as an antecedent factor of approach-avoidance conflicts. Donovan et al. (1994, p. 284) only showed how the Mehrabian and Russell (1974) model related “features of the environment (S) to approach-avoidance behaviors (R) within the environment, mediated by the individual’s emotional states (O) aroused by the environment”. This study, therefore, builds on Foxall and Greenley’s (1999) and Foxall and Yani-de-Soriano’s (2005) earlier research which identified the important role of different contexts for applying the Mehrabian and Russell (1974) model; and this study provides a more nuanced picture of a wider range of antecedents. The emotional and behavioural aspects of consumers’ approach-avoidance conflicts differed between online and offline channels. The level of arousal is important in relation to the emotions which consumers experience and shapes subsequent consumers behavior. Overload is one of the key antecedents of consumer ambivalence where consumers feel “overwhelmed or ill-prepared during the purchasing process and the sheer volume of purchasing decisions to be made” (Otnes et al., 1997, p. 87). Traditional retail stores need strategies, which enhance customers’ experiences in-store so that shoppers experience positive emotions of pleasure, arousal and dominance. Bricks and mortar retailers have to monitor and manage customers’ feelings of
pleasure, arousal and dominance very carefully. Retail atmospherics can play a very important role in ensuring that offline environments offer just the right amount of stimulation to arouse consumers’ positive emotions, while avoiding either stimulating negative emotions or provoking no arousal at all (see Figure 1), thus confirming earlier research (Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005). Retailers need to reduce the impact of consumers’ emotional responses to the retail setting where mixed emotions are likely to lead to consumers leaving the store without making a purchase. Donovan and Rossiter (1982, p. 50) demonstrated that arousal was a “key mediator of intentions to spend time in the store” as well as a “positive relationship between arousal and affiliation. The implication of the arousal-affiliation relationship is that more aroused shoppers will be more likely to interact with other people in the store” and that in-store stimuli such as bright lights and rock music may increase arousal. Thus retail managers need to ensure their physical environment facilitates affiliation (either with staff or fellow shoppers or shopping partners (e.g. mothers, wives, sisters)). In contrast for online sites, consumers need to be enticed to return, explore and complete the purchase. Online channels are highly regarded during the search stage of the consumption process. The challenge is to ensure that potential customers return and make a purchase on a subsequent occasion online, rather than defecting after the successful completion of the search stage to the offline channel for the purchase completion stage. Online retailers need to provide positive experiences, which reinforce shoppers’ desire to return and explore the web site. This would clearly provide a potentially important way of countering the competitive advantage of bricks and mortar retailers’ strategies for stimulating shoppers’ intention to patronize traditional retail stores. The impact of the complexity and novelty of the environmental situation on shopping behavior confirmed and extended earlier work (Donovan et al., 1994; Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005) by showing that within approach-avoidance conflicts, online stores are perceived as more complex than offline stores (H2). This increases the challenge for web site designers and online retailers to ensure that all aspects of the shopping experience online is not perceived as too complicated, difficult, and overwhelming to cope with (e.g. clear signage and navigation buttons). Careful design of web sites, particularly user interfaces, will ensure that consumers find the online shopping experience to be relaxing rather than threatening. In online shopping channels, the sheer volume of decisions required while navigating the web site illustrates the impact of complexity on online shopping experiences. This suggests online retailers need to monitor carefully the complexity of their web sites so as not to deter shoppers. We extended earlier work on perceived risk (e.g. Chaudhuri, 1997) by showing that the perceived risk attached to product performance has a greater impact on approach-avoidance conflicts in online channels; whereas levels of product involvement (i.e. need for product; and exciting product) have a greater impact on approach-avoidance conflicts in offline stores (H4). For online retailers this means that they need to continue to invest in building trust into the customer-supplier relationship by a variety of mechanisms, concentrating particularly on the main sources of shopper concern, e.g. security in terms of the payment and delivery systems in order to reduce consumers’ approach-avoidance conflicts. Conversely, for offline channels it is product (rather than channel) performance which is at the heart of consumers’
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approach-avoidance conflicts, and so strategies are needed which reassure customers about product performance in order to alleviate the effect of perceived risk on approach-avoidance conflicts in offline channels. Our findings support the emphasis placed by earlier researchers on the importance of understanding the emotional drivers of behaviors in approach-avoidance conflicts (Donovan and Rossiter, 1982; Foxall and Greenley, 1999; Foxall and Yani-de-Soriano, 2005). We also extend earlier work on consumer ambivalence and the effect of mixed emotions on consumers’ experiences which often resulted “directly from interactions in, or structural features of, the marketplace” (Otnes et al., 1997, p. 80). We saw this with the impact of the complex online environment on shopping intentions. Supplementing recognized models of the consumption process with ambivalence promises to be a fruitful way of extending our understanding of consumer behaviour in both online and offline settings. Ambivalence derives from consumers’ feelings of mixed emotions, which can be linked to approach and avoidance motivational behaviours, identified in earlier studies (e.g. Donovan and Rossiter, 1982); and can be experienced either simultaneously or sequentially. Earlier research has established that ambivalence (i.e. mixed emotions) facilitate or frustrate consumers’ motivation to shop to varying degrees. These findings allow us to start to interpret further the role that mixed emotions play in consumer decision-making in retail environments, particularly when trying to compare approach-avoidance motivational conflicts across offline and online channels. As mentioned previously, no clear distinctions could be drawn between online and offline channels in terms of the mediating effects of consumers’ emotions on intention to purchase. This meant, for instance, that the impact of the store’s physical attributes (complexity and novelty) on intention to shop is mediated by consumers’ emotional states both offline and online, but the impact of the density of the store (e.g. if consumers find the environment is crowded or dominating) does not seem to be mediated by emotions (H5). The impact of the normative and informational influence of reference groups on consumers’ intention to purchase was mediated by consumers’ emotions (H6), both offline and online, but the impact of interpersonal influences on intentions to shop were not mediated by consumers’ emotional states. Consumers’ emotions also mediated the impact of certain product characteristics (e.g. product’s perceived social risk; and whether or not it is an exciting product) on their intentions to shop either offline or online, but other product related characteristics (e.g. financial or performance risk, or need for the product) did not seem to be mediated by consumers’ emotions (H7). An important challenge for retailers in both channels is to manage the potential flip-flopping between online and offline channels by consumers who clearly exploit the various strengths of each channel. Consumers have the ability to search on line but then make the purchase offline in order to manage perceived performance risk derived from negative feelings about the reliability and security of online channels, especially payment and delivery systems. This contrasts with consumers’ ability in brick and mortar stores to evaluate products, and then move to take advantage of the pricing strategies of online channels. Brick and mortar could either try and compete on price, which could be difficult in view of all the additional overheads carried by offline stores; or they could try and exploit the service advantage offered by many offline retailers, e.g. interpersonal relationships with store staff to tie customers into relationships, and
thus ensure that shoppers remain loyal customers throughout the consumption process and complete the final purchase with the bricks and mortar retailer. For online channels, exploiting competitive advantage involves offering some form(s) of reassurance to customers so that they can manage the security risks associated with channel performance (e.g. payment and delivery systems) which consumers perceive as associated with purchasing online. Both bricks and mortar and online retailers are equally effective during the early stages of the consumption process (i.e. searching for information, evaluating alternatives). The challenge is to build up consumer loyalty in these preliminary and preparatory stages so that consumers remain for the all important end game, i.e. making the purchase in that channel, rather than switching at the point of purchase from one channel to the other – as both the opportunity and real costs for retailers are, otherwise, to provide the services to support the early stages of consumer decision-making (i.e. search, choice) without reaping the benefits of winning the final purchase. Conclusion This exploratory and contextual international study confirmed that examining approach-avoidance conflicts promises to be a fruitful avenue for understanding both global and incremental aspects of ambivalence. This is important because of the effect of mixed emotions (ambivalence) on consumer behaviour in both offline and online shopping channels. We briefly review the theoretical and empirical conclusions from our study; the limitations; and potential directions for future research. Theoretically, we have followed Armitage and Conner (2000) in pursuing a global view of ambivalence. However, Chaiken et al. (1995) propose “three forms of evaluative inconsistency: cognitive-affective; affective-evaluative, and evaluative-cognitive” (Armitage and Conner, 2000, p. 1430). Adopting a more incremental view of ambivalence would allow more refined measures to be used to capture different types of evaluative inconsistency (Chaiken et al., 1995) in consumer decision-making, in both offline and online settings. By eliciting three separate facets, it might be possible to obtain a sharper focus on the “differential effects of levels of ambivalence” (Armitage and Conner, 2000, p. 1430) on consumer behaviour in retailing channels. Earlier research on ambivalence has also indicated that attitudes are based on separate positive and negative components and has problematized the conceptualization of attitudes and emotions along a bipolar continuum (Priester and Petty, 1996). Petty et al. (1997, p. 613)) have argued that the assumption that “positive and negative evaluative reactions are reciprocally activated” is not necessarily tenable but rather “positive and negative responses should be viewed as a bivariate evaluative plane” (Cacioppo and Berntson, 1994). Similarly, Babin et al. (1998, cited in Maxwell and Kover, 2003, p. 554) argue “that positive and negative affects are often but not always unipolar rather than bipolar dimensions . . . one cannot, consequently consider negative affect as simply the opposite of positive affect”. Cacioppo and Berntson (1994) also posit “the inability of traditional bipolar attitude scales to fully differentiate among these possibilities [i.e. positive and negative responses] and would suggest the value for future research to use separate measures of the positive and negative bases of attitudes” (Petty et al., 1997, p. 613). In designing our questionnaire we adopted the conventional assumption about bipolar scales for measuring attitudes. However, for future research we would suggest drawing on all these viewpoints and endeavouring
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to separate positive and negative aspects of attitudes into unipolar measurements for further studies of ambivalence and mixed emotions. For other future research, it is important to assess the feeling states that are brought to the store (Dawson et al., 1990) as well as the feeling states induced by the store, as explored by earlier studies which examined purchase behaviors (Donovan and Rossiter, 1982). This is because ambivalence (mixed emotions) is just as susceptible to the influence of recall and pre-existing experiences as it is to new experiences in the retail channel. Updegraff et al. (2004) also argued that past negative emotional experiences may be “chronically salient sources” for subsequent experiences, indicating that future studies of approach-avoidance conflicts should elicit the all-important feelings and memories that consumers bring to their experiences of shopping in both offline and online channels as these pre-existing feelings contribute to the priming of consumers for their next set of shopping experiences. Ambivalence needs to be understood therefore, not just in both global and incremental terms, but also within the longitudinal setting of consumers’ histories and memories of their earlier shopping experiences across both channels. For this empirical study we used convenience sampling, which limits the generalizability of the results; and also the capacity to perform cross-country analyses to bring out any potential variations among European countries. Future research could usefully seek more representative country samples to allow cross-country comparisons, and also generalization from the findings. In terms of cross-cultural validation, future studies should test whether the proposed model is culturally invariant, given similar behaviour settings are selected. The main focus in the current study was to use an international sample to test the conceptual model (see Reynolds et al., 2003), and not to compare the respective countries regarding their purchase intentions, which imply certain limitations. Within the context of the cultures used for this study (Austria, Greece and the UK) a larger scale study might be able to draw out differences in shopping propensities for online and offline retailing environments between Austrian, Greek and UK consumers; and link these differences to the role of mixed emotions (i.e. ambivalence) in approach-avoidance conflicts in retailing. Drawing on earlier cross-cultural research (e.g. Hofstede, 2001; Schwartz and Zanna, 1992) it would be interesting to examine how far the five dimensions (power distance index (PDI); individualism (IDV); masculinity (MAS); uncertainty avoidance (UAI); and long term orientation (LTO)) as well as values vary across national contexts between the specific retailing contexts of online or offline shopping. In terms of masculinity for instance, do cultures which score low on masculinity (and thus high on femininity) demonstrate similar sets of behaviours when shopping offline and online, or do the sociable aspects of shopping mean that a distinction can be drawn so that shopping offline is identified as preferred to shopping online, whereas cultures high on masculinity scores prefer online to offline shopping environments? In terms of uncertainty avoidance, do consumers’ perceptions of risk related to purchasing lead to greater aversion to shopping on line and a greater preference for offline shopping environments in cultural contexts which score high on uncertainty avoidance (e.g. Greece). Expanding beyond the immediate cultural contexts for this empirical study (Austria, Greece and the U.K.) would allow selection of participant countries on the basis of national scores on these dimensions for further study. Different countries might potentially provide more nuanced insights into how
cultural contexts influence shopping propensities for online and offline retailing environments; and highlight differences in the role of mixed emotions (i.e. ambivalence) in approach-avoidance conflicts in culturally different retailing contexts. Another important direction for future study would be to incorporate more detailed and explicit psychological elements into the model building. As this was an exploratory study, we concentrated on developing and testing three selected influences on purchase intention: situation, product and reference group in this initial stage of theory building. This allowed us to focus on three specific topic areas and measure the causal relations among and mediating effects of approach-avoidance on these constructs. However individual determinants play an important and complex role within retailing contexts particularly in predicting purchase intentions, and so psychological factors will impact the operation of mixed emotions and ambivalence. Thus including psychological factors in the next stage of model development and theory testing could potentially provide a more nuanced understanding of the impact of consumers’ mixed emotions on approach-avoidance conflicts. Note 1. In our model we explore how independent variables (situation, product, reference group) have an impact on purchase intentions via emotional states, i.e. emotional states intervene (mediation effect). We are not assuming that emotional states influence the relation of the independent variables (situation, product, reference group) and the intention to purchase, which would mean they cause the intention (Baron and Kenny, 1986).
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