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CONSUMER CONFUSION MODERATES THE INERTIA-PURCHASE INTENTION RELATIONSHIP.

The past few decades have seen increasing growth of convenience stores in Taiwan, which has Asia Pacific's--and perhaps the world's--highest density of convenience stores per person (Huang & Huang, 2016). Convenience store chains have been competing for market share and the chain operators continue to explore prospects for expansion. The abundance of information about and alternatives to choose from regarding convenience stores, however, cause consumers to become increasingly confused about which store brand to patronize. As the complexity of choice rises, consumers tend to simplify their decision-making process by relying on simple heuristics (Iyengar & Lepper, 2000) or inertia (Turnbull, Leek, & Ying, 2000) to make purchase decisions.

However, to the best of our knowledge, no previous researchers have examined the effects of consumer inertia on purchase behavior under the condition of confusing decision making. In response to this gap in the consumer choice literature, we investigated the moderating effect of consumer confusion on the relationship between consumer inertia and purchase intention.

Literature Review

Consumer Inertia and Purchase Intention

Consumer inertia reflects consumers undertaking repeated purchasing behavior passively and without much thought (White & Yanamandram, 2004), and indicates the persistence of existing attitude or behavioral patterns, sometimes even if there are better alternatives or incentives to change (Bozzo, 2002; Kuo, Hu, & Yang, 2013; Polites & Karahanna, 2012). Inertia has been described as a multidimensional construct comprising the interrelated, rather than equivalent, dimensions of cognitive and behavioral inertia (Anderson & Srinivasan, 2003; Oreg, 2003; Piderit, 2000). Cognitive inertia refers to the tendency to consciously resist change and continue with a purchase decision, whereas behavioral inertia indicates the unconscious continuance of an explicit behavior (Barnes, Gartland, & Stack, 2004).

In the context of the Taiwanese retailing industry, the four major convenience store chains are very similar in nature (e.g., goods, services, and store layouts). Thus, consumers choose a convenience store simply because of lower transaction costs, habit, and/or trust (Lee & Cunningham, 2001; Shiu, 2017). However, the psychological commitment or preference in an individual's cognitive choice becomes embedded in their behavioral routines, eventually becoming behavioral inertia (Greenfield, 2005). As a result, consumers' cognition and buying behavior become stable and they tend to habitually avoid change or variety (Bozzo, 2002). In this situation, consumers rely more on their prior experience or past behavior to make a buying decision (Bawa, 1990). Although firms seek to differentiate themselves from competitors, consumers depend more on what they have always done in the past to guide their perception and purchasing intentions (Polites & Karahanna, 2012).

The Moderating Role of Consumer Confusion

Consumer confusion has been a central issue in the consumer literature for about two decades. The concept of confusion, which may be classified into three types--similarity, overload, and ambiguity (Walsh, Hennig-Thurau, & Mitchell, 2007)--is a disturbed psychological state characterized by overly rich and duplicitous intelligence that affects information processing in decisionmaking contexts. Consumers may be either aware or unaware of their confusion (Mitchell & Papavassiliou, 1999); regardless, confusion represents the failure to develop a correct interpretation of various facets of a product or service during the information processing procedure (Turnbull et al., 2000).

When consumers experience confusion with products and services, they can be confronted with conflicting and ambiguous choices (Walsh & Mitchell, 2010) and become less capable of making rational buying decisions when choosing which store offers the best quality or value (Mitchell & Papavassiliou, 1999). Consequently, consumers rely more on cognitive or behavioral inertia to make a purchase decision (Bawa, 1990; Piderit, 2000), so that when they experience confusion they rely on their belief system (e.g., prior experience or store knowledge) or behavioral routine (Robertson, 1976), as the easiest option. In other words, consumers will not spend too much effort on evaluating various alternatives; instead, they will rely more on internal sources of information. On the other hand, if consumers encounter less complex and less ambiguous information, they may easily distinguish differences in products and services, and tend to make a more rational purchase decision. In this case, the role of inertia may have less of an effect on consumers' decision making. On the basis of the above discussion, we hypothesized that consumer confusion will positively moderate the relationship between consumer inertia and purchase intention.

Method

Participants and Procedure

This study was approved by the National Cheng Kung University Governance Framework for Human Research Ethics in Taiwan. Information to ensure informed consent was on the first page of the first questionnaire. Any participant could withdraw at any time.

Empirical data were collected at two sampling stages. In the first stage, a draft questionnaire was developed and tested to establish the validity of the measures. In the second stage, a formal survey was conducted to collect empirical data from participants in southern and central Taiwan. We used two research assistants who were trained to conduct the survey to distribute the questionnaires, which were returned to them later. Customers were approached randomly as they finished shopping at a convenience store in central Taiwan and two convenience stores in southern Taiwan in August 2014. Among the 170 questionnaires issued at the formal survey stage, 166 valid responses were received (approximately 97.6%). Participants comprised 88 males (53%) and 78 females (47%); 24.7% held high school degrees or below, 65.7% held college degrees, and 9.6% held graduate degrees. In terms of age, 7.8% were under 20 years old, 46.4% were 21-30, 24.7% were 31-40, 6.6% were 41-50, and 14.5% were over 50 years old. Regarding monthly income, 26.5% earned under US$650, 41% earned US$651-US$1,300, 16.9% earned US$1,301-US$1,950, and 15.7% earned US$1,951 or over. It was our observation that the characteristics of the participants reflected those of consumers at the convenience store markets.

Measures

The final scale consisted of 20 items divided across six factors, comprising three types of confusion, two types of inertia, and purchase intention, which together explained 73.91% of the total variance. The Cronbach's coefficient alpha for the internal reliability of the multi-item measures was .89, and for the individual constructs ranged between .72 and .89, indicating good internal consistency (George & Mallery, 2003). All items were measured on a 5-point Likert scale (1 = strongly agree, 5 = strongly disagree).

We used 10 items adapted from Walsh et al. (2007), Walsh and Mitchell (2010), and Matzler, Stieger, and Fuller (2011) to measure the three dimensions of similarity confusion ([alpha] = .89), overload confusion ([alpha] = .78), and ambiguity confusion ([alpha] = .72).

We used six items adapted from Piderit (2000), Barnes et al. (2004), and Gupta, Su, and Walter (2004) to measure the two dimensions of cognitive inertia ([alpha] = .75) and behavioral inertia ([alpha] = .80).

To measure purchase intention, we used a four-item scale ([alpha] = .82) adapted from Dodds, Monroe, and Grewal (1991) and Sweeney, Soutar, and Johnson (1999).

Control variables. Several demographic characteristics of the respondents were controlled for in our analysis. Gender was coded as 0 = female and 1 = male. Level of education was coded using three categories (0 = high school or below, 1 = undergraduate, and 2 = graduate or above). Age was coded based on five categories (0 = under 20 years, 1 = 21-30 years, 2 = 31-40 years, 3 = 41-50 years, and 4 = 51 years or over). Monthly income was measured using four categories (0 = under US$650, 1 = US$651-US$1,300, 2 = US$1,301-US$1,950, and 3 = US$1,951 or over).

Results

Model Fit

The relationships among the constructs were tested using Amos 20. A good model fit is indicated when the chi square/degrees of freedom ratio ([chi square]/df is 3 or less; the goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI), and Tucker-Lewis index (TLI) are above .90; and the root mean square error of approximation (RMSEA) is below .08 (Hair, Black, Babin, & Anderson, 2010). The goodness-of-fit indices for the hypothesized model were as follows: [chi square]/df = 2.01 ([chi square] = 24.08, df = 12, p < .001), GFI = .95, CFI = .96, NFI = .92, TLI = .91, and RMSEA = .04, all of which met the acceptability criteria. These results indicate that the conceptual model was reasonably consistent with the data.

Reliability and Validity

We tested the reliability and validity of the measures by calculating the composite reliability (CR) and average variance extracted (AVE). The constructs of consumer inertia, purchase intention, and consumer confusion all had CR values of .98, indicating good reliability. Further, all values of AVE (consumer inertia = .86, purchase intention = .85, consumer confusion = .88) exceeded .50, and the square roots of AVE (consumer inertia = .93, purchase intention = .92, consumer confusion = .94) were greater than the Pearson correlations between the constructs (consumer inertia and purchase intention: r = .56, consumer inertia and consumer confusion: r = .47, purchase intention and consumer confusion: r = .44; Hair et al., 2010), indicating that the convergent and discriminant validity were adequate.

Hypothesis Testing

Following the procedure recommended by Baron and Kenny (1986), we used hierarchical multiple regression to determine how much additional variance was explained by the independent variables after controlling for the demographic variables (i.e., gender, age, level of education, and income), main effects of consumer inertia and confusion, and interaction effect between inertia and confusion. As can be seen in Table 1, the results show that consumer inertia ([beta] = .45, t = 6.33, p < .001) and consumer confusion ([beta] = .22, t = 3.06, p < .01) were positively related to purchase intention (see Model 2), and the interaction effect between consumer inertia and confusion was also positively related to purchase intention ([beta] = .15, t = 2.19, p < .05; see Model 3). Thus, our hypothesis was supported.

Discussion

The empirical results show that consumers rely on their inertia in relation to purchase intentions, and that the relationship is strengthened when consumers experience confusion with retailer brands in the marketplace. Unlike previous researchers, who suggested that consumer inertia does not apply to lower-quality brands (Corstjens & Lal, 2000) and that consumers can easily adapt to new shopping behaviors if they feel treated unfairly (Kuo et al., 2013), we found that, under conditions of confusion, consumers' decision making may be more reliant on their inertia. This means that consumers in Taiwan are less capable of making rational buying decisions because none of the major convenience store retailers have succeeded in providing novel consumption experiences or alternatives to the existing convenience store model.

However, consistent with the existing literature, we found that consumers tend to simplify their decision-making process as the complexity of making choices rises (Iyengar & Lepper, 2000). Thus, cognitive and behavioral inertia determine decision-making behaviors under conditions of confusion (Bawa, 1990; Bozzo, 2002; Polites & Karahanna, 2012), and brands become closely tied to consumers' belief systems and behavioral routines (Greenfield, 2005; Robertson, 1976), minimizing the effects of alternative attraction or incentives to change. Therefore, substantial differentiation in marketing and communication is critical for convenience store chains to strengthen consumers' belief systems and behavioral routines, help to build strong store brand images and consumer trust, and capture a competitive advantage and greater share of the market in both the short and long term.

Limitations and Directions for Future Research

There are some limitations to our conceptual model of confusing decision making in a Taiwanese retail context, which may be addressed in future research. In this study, we focused on the relationships among retailing-relevant purchasing determinants in the convenience store industry. Given the great diversity of retail categories, these findings must be tested in different sectors and other countries owing to the great diversity of values and cultures in other domains. Future researchers might consider exploring how consumers' belief system influences the endorsement of and/or preference for a brand in a convenience store chain.

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JERRY YUWEN SHIU

Tajen University

SHIAN-YANG TZENG

Shantou University

Jerry Yuwen Shiu, Department of Marketing and Distribution Management, Tajen University; Shian-Yang Tzeng, Business School and Institute of Guangdong and Taiwan, Shantou University. This study was supported by the Ministry of Science and Technology in Taiwan (MOST 104-2221E-127-007 to the first author) and the Institute of Guangdong and Taiwan in China (ZDXM201602 to the second author). The authors thank Wilson VanThac Dang for assistance with data collection and analyses.

Correspondence concerning this article should be addressed to Shian-Yang Tzeng, Business School and Institute of Guangdong and Taiwan, Shantou University, 243 Daxue Road, Shantou, Guangdong 515063, People's Republic of China. Email: xyzeng@stu.edu.cn

https://doi.org/10.2224/sbp.6792
Table 1. Hierarchical Multiple Regression Results for Testing of
Hypotheses

Variables                           Purchase intention
                        Model 1     Model 2              Model 3

Control variables
  Gender                 .078         .003                 .006
  Age                    .140         .146                 .135
  Level of education     .062         .017                -.003
  Income                -.258 (*)    -.155                -.137
Main effects
  Consumer inertia                    .453 (***)           .444 (***)
  Consumer confusion                  .223 (**)            .292 (***)
Interaction effect
  Inertia x Confusion                                      .152 (*)
[R.sup.2]                .040         .370                 .389
Adj. [R.sup.2]           .017         .347                 .362
F                       1.692       15.596 (***)         14.369 (***)
[DELTA][R.sup.2]                      .330                 .019

Note. N = 166. (*) p < .05, (**) p < .01, (***) p < .001.
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Author:Shiu, Jerry Yuwen; Tzeng, Shian-Yang
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9TAIW
Date:Mar 1, 2018
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