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Effects of social influence on consumers' voluntary adoption of innovations prompted by others
Journal of Business Research 64 (2011) 1190–1194
Contents lists available at ScienceDirect
Journal of Business Research
Effects of social influence on consumers' voluntary adoption of innovations prompted by others ☆ Sang-Hoon Kim 1, Hyun Jung Park ⁎ Seoul National University, Graduate School of Business, 599 Gwanak Ro, Gwanak-gu, Seoul 151-916, Republic of Korea
a r t i c l e
i n f o
Article history: Received 1 September 2010 Received in revised form 1 January 2011 Accepted 1 March 2011 Available online 14 July 2011 Keywords: Innovation adoption Social influence Duration analysis Prompted adoption
a b s t r a c t Research on innovation adoption focuses on voluntary adoption, although non-voluntary or prompted adoption decisions are prevalent in real life, especially for high-tech products and services. This study aims to investigate the effect of social influence on consumers' innovation adoption in the context of prompted adoption. In particular, the present paper models the duration of voluntary adoption as a function of social norms, attractiveness of the prompter, number of prompters, and so on. Prior knowledge is not only a control variable, but also a moderating variable for a few social factors. This paper validates models relying on the illustrative application of a mobile gift service called Gifticon. The results provide much insight for marketing practitioners on how to accelerate consumers' adoption behavior and therefore the diffusion of innovative products. © 2011 Elsevier Inc. All rights reserved.
1. Introduction People nowadays prompt others to adopt what they have just bought, especially high-tech products or services. Prompters not only make recommendations by positive word-of-mouth, but they also urge other people to experience innovative new products. They sometimes go a step further and give their friends innovative products as gifts, because things that are new and different can make useful, practical and fun gifts. Various levels of prompting behavior of people influence others, and the behavior often plays a key role in consumers' adoption of innovations. Research in the area of innovation adoption focuses mainly on the voluntary adoption process in which consumers make their own decisions. However, non-voluntary or prompted adoptions are prevalent in real-life situations. In addition, most studies on innovation adoption investigate the roles of product characteristics and personal traits as drivers of consumers' adoption decisions. But considerable anecdotal evidence indicates that social influence is far more important in the decision-making process, especially in the context of prompting behavior. Previous studies use subjective norms to capture the essence of social influence, with mixed results. For example, the subjective norm only has a significant impact on technology acceptance under mandatory settings (Venkatesh & Davis, 2000). Some researchers
☆ The authors acknowledge the financial support provided by The Institute of Management Research at Seoul National University for this research. ⁎ Corresponding author. Tel.: + 82 10 2601 3904. E-mail addresses: [email protected] (S.-H. Kim), [email protected] (H.J. Park). 1 Tel.: + 82 2 880 6934. 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.06.021
criticize that this approach only deals with restricted normative components and does not reflect wider societal contexts (Conner & Armitage, 1998). In light of such problems, the current study examines the effects of the quantity and quality dimensions of social influence on consumers' innovation adoption behavior when prompting is present. This study pays particular attention to how social factors related to prompters affect the time until consumers' voluntary adoption after being induced to use an innovative mobile service by prompters. This paper models and empirically validates the relationships among several constructs such as adoption timing, social influence, and other control variables. 2. Literature 2.1. Adoption of innovation Diffusion is “the process by which an innovation is communicated through certain channels over time among the members of social systems” (Rogers, 1995). Diffusion of innovation theory (Rogers, 1995) explains how people adopt new ideas or innovations and proposes five key attributes of an innovation that affect a consumer's adoption decision: relative advantage, compatibility, complexity, trialability, and observability. Researchers also suggest that a social system beyond the individual's and the innovation's idiosyncratic characteristics influences adoption decisions (e.g., Cooper & Zmud, 1990). A common explanation is that potential adopters feel uncertain about an innovation's expected consequences. Individuals are generally uncomfortable with uncertainty, and will, therefore, tend to interact
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with people in their social network to consult them on their adoption decisions (Katz & Tushman, 1979).
Social Norms from the Prompter
2.2. Social influence Several researchers include social influence or social pressure in their technology acceptance models. Venkatesh, Davis, Morris, Davis, and Davis (2003) identify social influence as a key construct that influences both usage intention and usage behavior in their unified theory of acceptance and use of technology (UTAUT) model. Although the scholars don't find any social influence in a voluntary (vs. mandatory) setting, social norms play an important role in consumer adoption of technology. Interpersonal influences come from a variety of people, such as neighbors, relatives, family members, and friends, as well as inspirational figures in the media, such as sports heroes or movie stars (Kulviwat, Bruner, & Al-Shuridah, 2009). In explaining the impact of social factors, most studies rely on the subjective norm (Cooper & Zmud, 1990), a person's perception that most people who are important to him/her think he/she should or should not perform a certain behavior (Fishbein & Ajzen, 1975). From the social psychological and economic perspectives, researchers distinguish two types of social influence: social norms and network effects. Theories of conformity in social psychology suggest that group members tend to comply with the group norm (Lascu & Zinkhan, 1999). On the other hand, economists believe that the effects of network externality demonstrate the impact of social influence in technology adoption behavior (Hsu & Lu, 2004). Consequently, the findings lead researchers to question whether the concept of subjective norm captures the full extent of social influence (Lee, Lee, & Lee, 2006). 3. Conceptual model and hypotheses 3.1. Conceptual model Social influence may be a critical element in consumers' decisionmaking, especially in the context of prompted adoption. Experiencing a product due to a prompting behavior from a significant other has a greater impact on what action a potential adopter chooses to take than anything else. Specifically, this study examines the effect of the prompters' (or gift-givers') social influence on the targets' (or gift-receivers') adoption of a mobile service called ‘Gifticon’. People across the world commonly exchange gifts, but tech-savvy South Koreans are now turning to mobile phones to do so thanks to the new mcommercial service. With Gifticon, people can send gift vouchers for merchants such as Starbucks, Haagen-Dazs, and McDonalds to their friends through instant messenger or on the web. Those who receive the messages can download the coupons with embedded barcodes and exchange them at the stores for real products (Businessweek, 2008). Though scholars acknowledge social influence on technology acceptance behavior, the effect of social influence needs further examination in various contexts. This paper explores the impact of social factors on the time when prompted consumers voluntarily adopt new products or services on their own. In particular, the current study incorporates into the model social factors such as social norms and attractiveness of the prompters (quality of social influence), and the number of prompters (quantity of social influence). Control variables are innovation characteristics, prior knowledge, and time since launch until prompt (Fig. 1). 3.2. Hypotheses 3.2.1. Social norms from the prompter Social norms consist of two distinct elements: informational influence, which occurs when a user accepts information obtained
Attractiveness of the Prompter Number of Prompters Innovation Characteristics
1191
H1
H2 H3
Duration of voluntary adoption after being prompted
H4 H5
Prior knowledge
H6
Time since Launch until prompt
Fig. 1. Conceptual model. Notes: the scale ranges from 1 to 38.
from other users as evidence about reality, and normative influence, which takes place when a person conforms to the expectations of others to obtain a reward or avoid a punishment (Deutsch & Gerard, 1955). These two kinds of influence generally operate through three distinct processes—internalization, identification, and compliance. That is, informational influence works through an internalization process when a user perceives information as enhancing his or her knowledge (Kelman, 1961). Normative influence corresponds to the stages of identification and compliance, because identification occurs when a user adopts an opinion held by others to define him/herself as related to the group, and compliance occurs when a user conforms to the expectations of others to receive a reward or to avoid rejection. The literature on interpersonal influence examines the influence of reference group members on brand choice and finds that group members tend to conform to the brand selected by the group (Bearden & Etzel, 1982). Individuals may develop values and standards for their behavior by referring to information, normative practices, and value expressions of a group or other individuals (Bearden & Etzel, 1982; Park & Lessig, 1977). H1: The higher the social norms from the prompter, the shorter the duration until the target person's voluntary adoption. H1-1: The higher the informational influence from the prompter, the shorter the duration until the target person's voluntary adoption. H1-2: The higher the normative influence from the prompter, the shorter the duration until the target person's voluntary adoption. 3.2.2. Attractiveness of the prompter In general, attractiveness facilitates persuasion (McGuire, 1969). The source attractiveness model posits that the effectiveness of communication depends on the source's attractiveness, characterized by familiarity, likeability and similarity. Familiarity indicates knowledge of the source through exposure, whereas likeability is affection for the source due to the source's physical appearance and behavior. Similarity is the supposed resemblance between the source and the receiver of the message (McCracken, 1989). Supposedly, the source characteristics play a key role in the process of identification. The crucial point in adopting the position urged by the source is whether the receiver can enhance his/her self-esteem through identification with the source. H2: The more attractive the prompter, the shorter the duration until the target's voluntary adoption. H2-1: The more familiar the prompter, the shorter the duration until the target's voluntary adoption. H2-2: The more likeable the prompter, the shorter the duration until the target's voluntary adoption. H2-3: The more similar to the target the prompter is, the shorter the duration until the target's voluntary adoption.
1192
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3.2.3. Number of prompters Communication itself within a social network is influential, but the level of interaction or intensity of communication also plays a significant role (Wasserman & Faust, 2004). In the same vein, researchers show that buzz volume positively impacts consumers' adoption decisions (Godes & Mayzlin, 2004). As innovations are often uncertain, ambiguous, and risky (Menzel & Katz, 1955), individuals turn to others who have had prior experience to learn more about them, find out how much they cost, and determine how effective they are (Becker, 1970). In the process, positive demand externalities and network effects play a role. Repeated prompts tend to increase perceived local network externalities and peer pressure, while diminishing uncertainty and risk. The current study includes the number of prompters as a variable to address not only network effects, but also the quantity of social influence. H3: The greater the number of prompters, the shorter the duration until the target's voluntary adoption. 3.2.4. Innovation characteristics Relative advantage, compatibility, and complexity are the most powerful determinants of innovation adoption (Tornatzky & Klein, 1982). Relative advantage is the degree to which an innovation is better than competing products. People more easily adopt and use innovations that have a clear, unambiguous advantage. Compatibility is the degree to which an innovation fits with the existing values, past experiences, and needs of potential adopters. The literature suggests that the more compatible the innovation is, the greater the likelihood of adoption (Gatignon & Robertson, 1991; Rogers, 1995). Complexity is the degree to which an innovation is difficult to understand and use. This study tries to confirm the roles of these variables in the context of prompted adoption. H4: The greater the perceived positive attribute of an innovation, the shorter the duration until voluntary adoption. H4-1: The greater the perceived relative advantage of an innovation, the shorter the duration until voluntary adoption. H4-2: The greater the perceived compatibility of an innovation, the shorter the duration until voluntary adoption. H4-3: The greater the perceived ease of use of an innovation, the shorter the duration until voluntary adoption.
sends a Gifticon to someone else (excluding reciprocating behavior to the original sender) since the target's first Gifticon-receiving experience. Each questionnaire item is measured on a five-point Likert scale. This study uses the scales by Park and Lessig (1977) to assess the social norms. That is, the respondents indicate how much they view their prompters as experts on the products or services and how much they actively seek information from them. Normative influence refers to the extent to which respondents were concerned about being criticized or commended by the prompters. The measures for the attractiveness of the prompter, McCracken (1989) provides, are degree of emotional familiarity, affection level based on the givers' appearance or behavior, and resemblance between the giver and the respondent in terms of the way of thinking and lifestyle. Table 1 summarizes three dimensions of innovation characteristics adapted for Gifticon. The reliability measure (Cronbach's alpha) for each scale is acceptable, with the lowest being satisfactory at 0.80. Prior knowledge refers to whether the consumer recognized Gifticon before receiving the mobile gift voucher for the first time. The duration from launch to prompt indicates the length of the time since launch of the Gifticon service until when the subject first received the Gifticon. 4.2. Analysis method This study uses a well-known method called survival analysis to test the hypotheses concerning the effect of social factors on the timing of consumers' voluntary adoption. Many respondents' voluntary adoption timing is not observable because they simply have not tried the Gifticon service themselves. In these cases, the incomplete nature of the observation, called a “right-censoring” problem, hinders researchers from using standard analysis methods such as regression analysis. Survival analysis can handle this type of problem. This paper models the likelihood of voluntary adoption by a hazard function, which is the rate at which the adoption event will occur at period t, upon the condition that it has not occurred by period t-1. hðt Þ = lim
Δt→0
3.2.5. Control variables Due to the existence of experiential attributes of innovations, consumers often perceive the value of the products or services to be ambiguous even after they experience them. When a trial leaves them uncertain in this way, consumers' evaluation of the experience may be subjective or less confident. Therefore, there is a possibility that prior knowledge influences the perception of the trial (Kempf & Smith, 1998). In addition, as time goes by, the adopters increase, and information such as product reviews or recommendations becomes available. H5: Knowledge of an innovation prior to the prompt will shorten the duration until the target's voluntary adoption. H6: The longer the time since the innovation's launch until the prompt to use the innovation, the shorter the duration until the target's voluntary adoption.
Prðt + Δt N T≥t jT≥t Þ f ðt Þ = Δt Sðt Þ
The commonly used specification is Cox's (1972) proportional hazard (PH) model, where the covariates have a multiplicative effect on the hazard function. According to Kleinbaum (1996), the Cox PH model is a safe choice when the researcher is uncertain about which parametric model to use. The proportional hazard at time t for an individual whose covariate vector is x is given by: hðt Þ = h0 ðt Þ expðα + xβÞ; Table 1 Measures of attributes of innovations. Attributes of innovations
Questionnaire items
References
Relative advantage
‘Gifticon’ improves my way of gift giving. ‘Gifticon’ has more merits than the gifts that I used to purchase. Learning to use ‘Gifticon’ is easy for me. I believe that ‘Gifticon’ is easy to use. Using ‘Gifticon’ fits well with the way of giving gifts. I think the gift receiver would regard ‘Gifticon’ as a sincere gift.
(Parthasarathy & Bhattacherjee, 1998; Rogers, 1995)
4. Research method 4.1. Data collection and instrument Only the consumers who ever received Gifticon, a mobile gift voucher, participate in the survey. This study uses 311 surveys out of 333 collected, after excluding the consumers who received mobile gifts from companies (not personal acquaintances). The dependent variable is the “duration until the target's voluntary adoption of Gifticon,” defined as the time until the moment the target customer
ð1Þ
Perceived ease of use
Compatibility
(Davis, 1989; Parthasarathy & Bhattacherjee, 1998) (Gatignon & Robertson, 1991; Rogers, 1995)
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120
1193
Table 3 Correlation table.
100 80 Censored data
60 Voluntary adopters
Normative influence Familiarity Similarity Likeability
Informational influence
Normative influence
0.449(**) 0.418(**) 0.410(**) 0.526(**)
0.369(**) 0.359(**) 0.409(**)
Familiarity
Similarity
0.573(**) 0.578(**)
0.502(**)
40
** Correlation is significant at the 0.01 level (2-tailed).
20
analysis drops the attractiveness variables in favor of social norms variables; however, social norms, taken separately or in combination, are still not significant. This result is in line with the empirical evidence that inclusion of this construct in the technology acceptance model yields somewhat mixed results (e.g., Davis, Bagozzi, & Warshaw, 1989; Mathieson, 1991). Another possible explanation is that the level of consumer involvement in the focal service, which is the value of the mobile gift voucher, is not high enough to influence them as decision makers. At the time of the survey, the prices of the products which the vouchers are for are generally low, ranging from one to ten dollars. A still simpler explanation is that because voluntary adoption of the service is to send a Gifticon to someone else (excluding reciprocating behavior), consumption of the innovation is not that socially visible, especially to the prompter. The degree to which a consumer believes that consumption of a product is socially visible moderates the effect of social norms on purchase intentions (Kulviwat et al., 2009). The result supports H2. In particular, familiarity (H2-1) and likeability (H2-3) turn out to be influential in accelerating the adoption. But similarity (H2-2) is not effective. This study also supports H3, indicating that the greater the number of prompters, the faster the voluntary adoption. This finding provides a logical ground for the usefulness of sampling innovative new products within the local network of potential consumers. Among innovation characteristics (H4), only the perceived relative advantage of the innovation significantly accelerates the duration of adoption, while the others do not. As for H5, prior knowledge turns out to play a very important role in accelerating the adoption. The magnitude of its effect is (0.597– 1) × 100% = − 40.3%, meaning that a one-level increase in prior knowledge decreases the duration by 40%. This figure suggests that prompting behavior is very effective when the target consumer has prior knowledge about the innovation. Fig. 3 compares the two accumulated hazard functions, one with prior knowledge and the other without knowledge.
0 1
3
5 7
9 11 13 15 17 19 21 23 25 27 29 31 33
Notes: the scale ranges from 1 to 38. Fig. 2. Histograms of duration of adoption process (months).
where h0(t) is the baseline hazard, α is an intercept and β are coefficients of variables. The likelihood that voluntary adoption occurs is a function of a baseline hazard that captures the effect of time since prompted adoption and the independent variables. The magnitude of the effect of each independent variable, when increased by one unit, is (e β − 1) × 100%. 5. Results Out of 311 respondents, 234 people (71.1%) adopted the service voluntarily, meaning that they have sent Gifticon to other people at least once. The remaining 78 subjects are right-censored observations, since voluntary adoption had not occurred by the time of the survey. Though more than 100 respondents adopted the innovation within one month after being prompted, the mean and the standard deviation of the duration until adoption (for those who adopted) are 5.4 and 7.3 months, respectively (See Fig. 2). Table 2 shows the estimation results. The Cox model yields a significant model chi-square, implying that at least one predictor coefficient is not zero (p b 0.001). Most of the hypothesized relationships prove to be valid (see Table 2). However, H1 is an exception. The result shows that both informational and normative social norms do not significantly affect the duration of voluntary adoption. One possible explanation is that multicollinearity exists between the social norm and the attractiveness of the prompter. In Table 3, social factors, such as social norms and attractiveness of the prompters, correlate highly. An additional
Table 2 Results of survival analysis. Construct
Variable
Model 1 Coefficient
Hazard ratio
Coefficient
Hazard ratio
Social norm
Informational influence Normative influence Familiarity Similarity Likeability Number of prompters Relative advantage Ease of use Compatibility No prior knowledge Time since launch till prompt No prior knowledge × familiarity No prior knowledge × likeability No prior knowledge × number of prompters
− 0.031 0.021 0.209⁎
0.969 1.022 1.233 0.927 1.243 1.022 1.237 0.986 0.891 0.597 1.017
− 0.089 0.037 0.108 − 0.050 0.228⁎ 0.018⁎⁎ 0.211⁎ − 0.014 − 0.105 − 2.697⁎⁎ 0.016⁎⁎ 0.434⁎
0.915 1.037 1.114 0.951 1.256 1.018 1.235 0.986 0.900 0.067 1.016 1.544 1.064 1.111
Attractiveness
Number of prompters Attributes of innovation
Control variables Interactions
Model χ
2
⁎ p b 0.10. ⁎⁎ p b 0.05.
− 0.076 0.218⁎ 0.021⁎⁎ 0.213⁎ − 0.014 − 0.115 − 0.515⁎⁎ 0.016⁎⁎
41.741 (p = 0.000)
Model 2
0.062 0.105⁎⁎ 50.225 (p = 0.000)
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another venue for improvement. In addition, testing distinctive dimensions regarding the attractiveness of prompters of high-tech products or services would be a possible research agenda. Lee and Lee (2003) identify two dimensions, sensuality and cuteness, in apparel models' attractiveness. Their empirical results show that each dimension of the attractiveness variable plays a different role in enhancing model-apparel match-up in advertisements. In addition, developing more comprehensive theoretical models with variables regarding personal and social factors would be a valuable attempt.
References
Fig. 3. Accumulated hazard function.
Model 2 of Table 2 takes into account the moderating role of prior knowledge on social influence. The difference between the two models (Model 1 and Model 2) is statistically significant (χ 2 = 8.484, d.f. = 3, p b 0.000). While the interaction effect of prior knowledge on likeability is not valid, prior knowledge moderates both familiarity and the number of prompters. That is, the effect of familiarity is more influential when the target consumer has no information on the innovative products or services. Likewise, the effect of the number of prompters on adoption duration is also greater when the customer has no prior knowledge of the innovation. 6. Discussion This study examines a rather under-explored issue of prompting behavior on innovation adoption. A key notion is that the result of prompting behavior (i.e., adoption) may vary depending on the social influence of the prompters. This study has theoretical as well as practical contributions. Theoretically, the study expands understanding of the non-voluntary adoption of innovation while at the same time answering the call for further studies on the role of social influence in innovation adoption behavior. This finding also demonstrates that the variables attractiveness and number of prompters capture distinct aspects of social influence on consumers' adoption of innovations. In terms of marketing implications, this study can help marketing managers better understand the determinants of innovations adoption in gift exchange settings, which may help them to establish better communication strategies. For example, marketers can promote innovation diffusion by positioning products as attractive gifts for people in close relationships, particularly for consumers without prior knowledge of the innovation. The current study is not without limitations, however. One cannot generalize the results of this study easily beyond the present product category. Follow-up studies using various innovations would be helpful to reconfirm the findings. A cross-cultural study would be
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Contents lists available at ScienceDirect
Journal of Business Research
Effects of social influence on consumers' voluntary adoption of innovations prompted by others ☆ Sang-Hoon Kim 1, Hyun Jung Park ⁎ Seoul National University, Graduate School of Business, 599 Gwanak Ro, Gwanak-gu, Seoul 151-916, Republic of Korea
a r t i c l e
i n f o
Article history: Received 1 September 2010 Received in revised form 1 January 2011 Accepted 1 March 2011 Available online 14 July 2011 Keywords: Innovation adoption Social influence Duration analysis Prompted adoption
a b s t r a c t Research on innovation adoption focuses on voluntary adoption, although non-voluntary or prompted adoption decisions are prevalent in real life, especially for high-tech products and services. This study aims to investigate the effect of social influence on consumers' innovation adoption in the context of prompted adoption. In particular, the present paper models the duration of voluntary adoption as a function of social norms, attractiveness of the prompter, number of prompters, and so on. Prior knowledge is not only a control variable, but also a moderating variable for a few social factors. This paper validates models relying on the illustrative application of a mobile gift service called Gifticon. The results provide much insight for marketing practitioners on how to accelerate consumers' adoption behavior and therefore the diffusion of innovative products. © 2011 Elsevier Inc. All rights reserved.
1. Introduction People nowadays prompt others to adopt what they have just bought, especially high-tech products or services. Prompters not only make recommendations by positive word-of-mouth, but they also urge other people to experience innovative new products. They sometimes go a step further and give their friends innovative products as gifts, because things that are new and different can make useful, practical and fun gifts. Various levels of prompting behavior of people influence others, and the behavior often plays a key role in consumers' adoption of innovations. Research in the area of innovation adoption focuses mainly on the voluntary adoption process in which consumers make their own decisions. However, non-voluntary or prompted adoptions are prevalent in real-life situations. In addition, most studies on innovation adoption investigate the roles of product characteristics and personal traits as drivers of consumers' adoption decisions. But considerable anecdotal evidence indicates that social influence is far more important in the decision-making process, especially in the context of prompting behavior. Previous studies use subjective norms to capture the essence of social influence, with mixed results. For example, the subjective norm only has a significant impact on technology acceptance under mandatory settings (Venkatesh & Davis, 2000). Some researchers
☆ The authors acknowledge the financial support provided by The Institute of Management Research at Seoul National University for this research. ⁎ Corresponding author. Tel.: + 82 10 2601 3904. E-mail addresses: [email protected] (S.-H. Kim), [email protected] (H.J. Park). 1 Tel.: + 82 2 880 6934. 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.06.021
criticize that this approach only deals with restricted normative components and does not reflect wider societal contexts (Conner & Armitage, 1998). In light of such problems, the current study examines the effects of the quantity and quality dimensions of social influence on consumers' innovation adoption behavior when prompting is present. This study pays particular attention to how social factors related to prompters affect the time until consumers' voluntary adoption after being induced to use an innovative mobile service by prompters. This paper models and empirically validates the relationships among several constructs such as adoption timing, social influence, and other control variables. 2. Literature 2.1. Adoption of innovation Diffusion is “the process by which an innovation is communicated through certain channels over time among the members of social systems” (Rogers, 1995). Diffusion of innovation theory (Rogers, 1995) explains how people adopt new ideas or innovations and proposes five key attributes of an innovation that affect a consumer's adoption decision: relative advantage, compatibility, complexity, trialability, and observability. Researchers also suggest that a social system beyond the individual's and the innovation's idiosyncratic characteristics influences adoption decisions (e.g., Cooper & Zmud, 1990). A common explanation is that potential adopters feel uncertain about an innovation's expected consequences. Individuals are generally uncomfortable with uncertainty, and will, therefore, tend to interact
S.-H. Kim, H.J. Park / Journal of Business Research 64 (2011) 1190–1194
with people in their social network to consult them on their adoption decisions (Katz & Tushman, 1979).
Social Norms from the Prompter
2.2. Social influence Several researchers include social influence or social pressure in their technology acceptance models. Venkatesh, Davis, Morris, Davis, and Davis (2003) identify social influence as a key construct that influences both usage intention and usage behavior in their unified theory of acceptance and use of technology (UTAUT) model. Although the scholars don't find any social influence in a voluntary (vs. mandatory) setting, social norms play an important role in consumer adoption of technology. Interpersonal influences come from a variety of people, such as neighbors, relatives, family members, and friends, as well as inspirational figures in the media, such as sports heroes or movie stars (Kulviwat, Bruner, & Al-Shuridah, 2009). In explaining the impact of social factors, most studies rely on the subjective norm (Cooper & Zmud, 1990), a person's perception that most people who are important to him/her think he/she should or should not perform a certain behavior (Fishbein & Ajzen, 1975). From the social psychological and economic perspectives, researchers distinguish two types of social influence: social norms and network effects. Theories of conformity in social psychology suggest that group members tend to comply with the group norm (Lascu & Zinkhan, 1999). On the other hand, economists believe that the effects of network externality demonstrate the impact of social influence in technology adoption behavior (Hsu & Lu, 2004). Consequently, the findings lead researchers to question whether the concept of subjective norm captures the full extent of social influence (Lee, Lee, & Lee, 2006). 3. Conceptual model and hypotheses 3.1. Conceptual model Social influence may be a critical element in consumers' decisionmaking, especially in the context of prompted adoption. Experiencing a product due to a prompting behavior from a significant other has a greater impact on what action a potential adopter chooses to take than anything else. Specifically, this study examines the effect of the prompters' (or gift-givers') social influence on the targets' (or gift-receivers') adoption of a mobile service called ‘Gifticon’. People across the world commonly exchange gifts, but tech-savvy South Koreans are now turning to mobile phones to do so thanks to the new mcommercial service. With Gifticon, people can send gift vouchers for merchants such as Starbucks, Haagen-Dazs, and McDonalds to their friends through instant messenger or on the web. Those who receive the messages can download the coupons with embedded barcodes and exchange them at the stores for real products (Businessweek, 2008). Though scholars acknowledge social influence on technology acceptance behavior, the effect of social influence needs further examination in various contexts. This paper explores the impact of social factors on the time when prompted consumers voluntarily adopt new products or services on their own. In particular, the current study incorporates into the model social factors such as social norms and attractiveness of the prompters (quality of social influence), and the number of prompters (quantity of social influence). Control variables are innovation characteristics, prior knowledge, and time since launch until prompt (Fig. 1). 3.2. Hypotheses 3.2.1. Social norms from the prompter Social norms consist of two distinct elements: informational influence, which occurs when a user accepts information obtained
Attractiveness of the Prompter Number of Prompters Innovation Characteristics
1191
H1
H2 H3
Duration of voluntary adoption after being prompted
H4 H5
Prior knowledge
H6
Time since Launch until prompt
Fig. 1. Conceptual model. Notes: the scale ranges from 1 to 38.
from other users as evidence about reality, and normative influence, which takes place when a person conforms to the expectations of others to obtain a reward or avoid a punishment (Deutsch & Gerard, 1955). These two kinds of influence generally operate through three distinct processes—internalization, identification, and compliance. That is, informational influence works through an internalization process when a user perceives information as enhancing his or her knowledge (Kelman, 1961). Normative influence corresponds to the stages of identification and compliance, because identification occurs when a user adopts an opinion held by others to define him/herself as related to the group, and compliance occurs when a user conforms to the expectations of others to receive a reward or to avoid rejection. The literature on interpersonal influence examines the influence of reference group members on brand choice and finds that group members tend to conform to the brand selected by the group (Bearden & Etzel, 1982). Individuals may develop values and standards for their behavior by referring to information, normative practices, and value expressions of a group or other individuals (Bearden & Etzel, 1982; Park & Lessig, 1977). H1: The higher the social norms from the prompter, the shorter the duration until the target person's voluntary adoption. H1-1: The higher the informational influence from the prompter, the shorter the duration until the target person's voluntary adoption. H1-2: The higher the normative influence from the prompter, the shorter the duration until the target person's voluntary adoption. 3.2.2. Attractiveness of the prompter In general, attractiveness facilitates persuasion (McGuire, 1969). The source attractiveness model posits that the effectiveness of communication depends on the source's attractiveness, characterized by familiarity, likeability and similarity. Familiarity indicates knowledge of the source through exposure, whereas likeability is affection for the source due to the source's physical appearance and behavior. Similarity is the supposed resemblance between the source and the receiver of the message (McCracken, 1989). Supposedly, the source characteristics play a key role in the process of identification. The crucial point in adopting the position urged by the source is whether the receiver can enhance his/her self-esteem through identification with the source. H2: The more attractive the prompter, the shorter the duration until the target's voluntary adoption. H2-1: The more familiar the prompter, the shorter the duration until the target's voluntary adoption. H2-2: The more likeable the prompter, the shorter the duration until the target's voluntary adoption. H2-3: The more similar to the target the prompter is, the shorter the duration until the target's voluntary adoption.
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3.2.3. Number of prompters Communication itself within a social network is influential, but the level of interaction or intensity of communication also plays a significant role (Wasserman & Faust, 2004). In the same vein, researchers show that buzz volume positively impacts consumers' adoption decisions (Godes & Mayzlin, 2004). As innovations are often uncertain, ambiguous, and risky (Menzel & Katz, 1955), individuals turn to others who have had prior experience to learn more about them, find out how much they cost, and determine how effective they are (Becker, 1970). In the process, positive demand externalities and network effects play a role. Repeated prompts tend to increase perceived local network externalities and peer pressure, while diminishing uncertainty and risk. The current study includes the number of prompters as a variable to address not only network effects, but also the quantity of social influence. H3: The greater the number of prompters, the shorter the duration until the target's voluntary adoption. 3.2.4. Innovation characteristics Relative advantage, compatibility, and complexity are the most powerful determinants of innovation adoption (Tornatzky & Klein, 1982). Relative advantage is the degree to which an innovation is better than competing products. People more easily adopt and use innovations that have a clear, unambiguous advantage. Compatibility is the degree to which an innovation fits with the existing values, past experiences, and needs of potential adopters. The literature suggests that the more compatible the innovation is, the greater the likelihood of adoption (Gatignon & Robertson, 1991; Rogers, 1995). Complexity is the degree to which an innovation is difficult to understand and use. This study tries to confirm the roles of these variables in the context of prompted adoption. H4: The greater the perceived positive attribute of an innovation, the shorter the duration until voluntary adoption. H4-1: The greater the perceived relative advantage of an innovation, the shorter the duration until voluntary adoption. H4-2: The greater the perceived compatibility of an innovation, the shorter the duration until voluntary adoption. H4-3: The greater the perceived ease of use of an innovation, the shorter the duration until voluntary adoption.
sends a Gifticon to someone else (excluding reciprocating behavior to the original sender) since the target's first Gifticon-receiving experience. Each questionnaire item is measured on a five-point Likert scale. This study uses the scales by Park and Lessig (1977) to assess the social norms. That is, the respondents indicate how much they view their prompters as experts on the products or services and how much they actively seek information from them. Normative influence refers to the extent to which respondents were concerned about being criticized or commended by the prompters. The measures for the attractiveness of the prompter, McCracken (1989) provides, are degree of emotional familiarity, affection level based on the givers' appearance or behavior, and resemblance between the giver and the respondent in terms of the way of thinking and lifestyle. Table 1 summarizes three dimensions of innovation characteristics adapted for Gifticon. The reliability measure (Cronbach's alpha) for each scale is acceptable, with the lowest being satisfactory at 0.80. Prior knowledge refers to whether the consumer recognized Gifticon before receiving the mobile gift voucher for the first time. The duration from launch to prompt indicates the length of the time since launch of the Gifticon service until when the subject first received the Gifticon. 4.2. Analysis method This study uses a well-known method called survival analysis to test the hypotheses concerning the effect of social factors on the timing of consumers' voluntary adoption. Many respondents' voluntary adoption timing is not observable because they simply have not tried the Gifticon service themselves. In these cases, the incomplete nature of the observation, called a “right-censoring” problem, hinders researchers from using standard analysis methods such as regression analysis. Survival analysis can handle this type of problem. This paper models the likelihood of voluntary adoption by a hazard function, which is the rate at which the adoption event will occur at period t, upon the condition that it has not occurred by period t-1. hðt Þ = lim
Δt→0
3.2.5. Control variables Due to the existence of experiential attributes of innovations, consumers often perceive the value of the products or services to be ambiguous even after they experience them. When a trial leaves them uncertain in this way, consumers' evaluation of the experience may be subjective or less confident. Therefore, there is a possibility that prior knowledge influences the perception of the trial (Kempf & Smith, 1998). In addition, as time goes by, the adopters increase, and information such as product reviews or recommendations becomes available. H5: Knowledge of an innovation prior to the prompt will shorten the duration until the target's voluntary adoption. H6: The longer the time since the innovation's launch until the prompt to use the innovation, the shorter the duration until the target's voluntary adoption.
Prðt + Δt N T≥t jT≥t Þ f ðt Þ = Δt Sðt Þ
The commonly used specification is Cox's (1972) proportional hazard (PH) model, where the covariates have a multiplicative effect on the hazard function. According to Kleinbaum (1996), the Cox PH model is a safe choice when the researcher is uncertain about which parametric model to use. The proportional hazard at time t for an individual whose covariate vector is x is given by: hðt Þ = h0 ðt Þ expðα + xβÞ; Table 1 Measures of attributes of innovations. Attributes of innovations
Questionnaire items
References
Relative advantage
‘Gifticon’ improves my way of gift giving. ‘Gifticon’ has more merits than the gifts that I used to purchase. Learning to use ‘Gifticon’ is easy for me. I believe that ‘Gifticon’ is easy to use. Using ‘Gifticon’ fits well with the way of giving gifts. I think the gift receiver would regard ‘Gifticon’ as a sincere gift.
(Parthasarathy & Bhattacherjee, 1998; Rogers, 1995)
4. Research method 4.1. Data collection and instrument Only the consumers who ever received Gifticon, a mobile gift voucher, participate in the survey. This study uses 311 surveys out of 333 collected, after excluding the consumers who received mobile gifts from companies (not personal acquaintances). The dependent variable is the “duration until the target's voluntary adoption of Gifticon,” defined as the time until the moment the target customer
ð1Þ
Perceived ease of use
Compatibility
(Davis, 1989; Parthasarathy & Bhattacherjee, 1998) (Gatignon & Robertson, 1991; Rogers, 1995)
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120
1193
Table 3 Correlation table.
100 80 Censored data
60 Voluntary adopters
Normative influence Familiarity Similarity Likeability
Informational influence
Normative influence
0.449(**) 0.418(**) 0.410(**) 0.526(**)
0.369(**) 0.359(**) 0.409(**)
Familiarity
Similarity
0.573(**) 0.578(**)
0.502(**)
40
** Correlation is significant at the 0.01 level (2-tailed).
20
analysis drops the attractiveness variables in favor of social norms variables; however, social norms, taken separately or in combination, are still not significant. This result is in line with the empirical evidence that inclusion of this construct in the technology acceptance model yields somewhat mixed results (e.g., Davis, Bagozzi, & Warshaw, 1989; Mathieson, 1991). Another possible explanation is that the level of consumer involvement in the focal service, which is the value of the mobile gift voucher, is not high enough to influence them as decision makers. At the time of the survey, the prices of the products which the vouchers are for are generally low, ranging from one to ten dollars. A still simpler explanation is that because voluntary adoption of the service is to send a Gifticon to someone else (excluding reciprocating behavior), consumption of the innovation is not that socially visible, especially to the prompter. The degree to which a consumer believes that consumption of a product is socially visible moderates the effect of social norms on purchase intentions (Kulviwat et al., 2009). The result supports H2. In particular, familiarity (H2-1) and likeability (H2-3) turn out to be influential in accelerating the adoption. But similarity (H2-2) is not effective. This study also supports H3, indicating that the greater the number of prompters, the faster the voluntary adoption. This finding provides a logical ground for the usefulness of sampling innovative new products within the local network of potential consumers. Among innovation characteristics (H4), only the perceived relative advantage of the innovation significantly accelerates the duration of adoption, while the others do not. As for H5, prior knowledge turns out to play a very important role in accelerating the adoption. The magnitude of its effect is (0.597– 1) × 100% = − 40.3%, meaning that a one-level increase in prior knowledge decreases the duration by 40%. This figure suggests that prompting behavior is very effective when the target consumer has prior knowledge about the innovation. Fig. 3 compares the two accumulated hazard functions, one with prior knowledge and the other without knowledge.
0 1
3
5 7
9 11 13 15 17 19 21 23 25 27 29 31 33
Notes: the scale ranges from 1 to 38. Fig. 2. Histograms of duration of adoption process (months).
where h0(t) is the baseline hazard, α is an intercept and β are coefficients of variables. The likelihood that voluntary adoption occurs is a function of a baseline hazard that captures the effect of time since prompted adoption and the independent variables. The magnitude of the effect of each independent variable, when increased by one unit, is (e β − 1) × 100%. 5. Results Out of 311 respondents, 234 people (71.1%) adopted the service voluntarily, meaning that they have sent Gifticon to other people at least once. The remaining 78 subjects are right-censored observations, since voluntary adoption had not occurred by the time of the survey. Though more than 100 respondents adopted the innovation within one month after being prompted, the mean and the standard deviation of the duration until adoption (for those who adopted) are 5.4 and 7.3 months, respectively (See Fig. 2). Table 2 shows the estimation results. The Cox model yields a significant model chi-square, implying that at least one predictor coefficient is not zero (p b 0.001). Most of the hypothesized relationships prove to be valid (see Table 2). However, H1 is an exception. The result shows that both informational and normative social norms do not significantly affect the duration of voluntary adoption. One possible explanation is that multicollinearity exists between the social norm and the attractiveness of the prompter. In Table 3, social factors, such as social norms and attractiveness of the prompters, correlate highly. An additional
Table 2 Results of survival analysis. Construct
Variable
Model 1 Coefficient
Hazard ratio
Coefficient
Hazard ratio
Social norm
Informational influence Normative influence Familiarity Similarity Likeability Number of prompters Relative advantage Ease of use Compatibility No prior knowledge Time since launch till prompt No prior knowledge × familiarity No prior knowledge × likeability No prior knowledge × number of prompters
− 0.031 0.021 0.209⁎
0.969 1.022 1.233 0.927 1.243 1.022 1.237 0.986 0.891 0.597 1.017
− 0.089 0.037 0.108 − 0.050 0.228⁎ 0.018⁎⁎ 0.211⁎ − 0.014 − 0.105 − 2.697⁎⁎ 0.016⁎⁎ 0.434⁎
0.915 1.037 1.114 0.951 1.256 1.018 1.235 0.986 0.900 0.067 1.016 1.544 1.064 1.111
Attractiveness
Number of prompters Attributes of innovation
Control variables Interactions
Model χ
2
⁎ p b 0.10. ⁎⁎ p b 0.05.
− 0.076 0.218⁎ 0.021⁎⁎ 0.213⁎ − 0.014 − 0.115 − 0.515⁎⁎ 0.016⁎⁎
41.741 (p = 0.000)
Model 2
0.062 0.105⁎⁎ 50.225 (p = 0.000)
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another venue for improvement. In addition, testing distinctive dimensions regarding the attractiveness of prompters of high-tech products or services would be a possible research agenda. Lee and Lee (2003) identify two dimensions, sensuality and cuteness, in apparel models' attractiveness. Their empirical results show that each dimension of the attractiveness variable plays a different role in enhancing model-apparel match-up in advertisements. In addition, developing more comprehensive theoretical models with variables regarding personal and social factors would be a valuable attempt.
References
Fig. 3. Accumulated hazard function.
Model 2 of Table 2 takes into account the moderating role of prior knowledge on social influence. The difference between the two models (Model 1 and Model 2) is statistically significant (χ 2 = 8.484, d.f. = 3, p b 0.000). While the interaction effect of prior knowledge on likeability is not valid, prior knowledge moderates both familiarity and the number of prompters. That is, the effect of familiarity is more influential when the target consumer has no information on the innovative products or services. Likewise, the effect of the number of prompters on adoption duration is also greater when the customer has no prior knowledge of the innovation. 6. Discussion This study examines a rather under-explored issue of prompting behavior on innovation adoption. A key notion is that the result of prompting behavior (i.e., adoption) may vary depending on the social influence of the prompters. This study has theoretical as well as practical contributions. Theoretically, the study expands understanding of the non-voluntary adoption of innovation while at the same time answering the call for further studies on the role of social influence in innovation adoption behavior. This finding also demonstrates that the variables attractiveness and number of prompters capture distinct aspects of social influence on consumers' adoption of innovations. In terms of marketing implications, this study can help marketing managers better understand the determinants of innovations adoption in gift exchange settings, which may help them to establish better communication strategies. For example, marketers can promote innovation diffusion by positioning products as attractive gifts for people in close relationships, particularly for consumers without prior knowledge of the innovation. The current study is not without limitations, however. One cannot generalize the results of this study easily beyond the present product category. Follow-up studies using various innovations would be helpful to reconfirm the findings. A cross-cultural study would be
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