In many situations, consumers have to make a sacrifice (e.g., price premium, loss of personal comfort and efficiency) when choosing a green product over its traditional alternative. Utilizing attribution theory, we offer a set of research propositions as an attempt to explain how consumers may rationalize their perceived sacrifice in making green purchases. First, we posit that consumers’ internal attribution (i.e., individual behaviors cause environmental degradation) enhances their green purchase behavior. Second, internal attribution would generate senses of self-efficacy and guilt, which lead to motivations to accept the perceived sacrifice in purchasing green products. Third, when green purchase indicates a significant sacrifice, consumers tend to develop a coping strategy by switching attribution of responsibility to others, and thus the influence of internal attribution on green purchase will be weakened.

This research tests the roles of vertical versus horizontal collectivism in affecting the relationships between place attachment, perceived environmental responsibility and pro-environmental behaviour. In doing so, the research addresses the boundary conditions for the attachment-behaviour link and provides guidance for implementing environmental policies using place attachment as the policy tool.

Introduction Compulsive buying refers to a condition where consumers make purchases repetitively and excessively (Ridgway, Kukar-Kinney & Monroe, 2008; Japutra, Ekinci & Simkin, 2017). Previous literature shows that two types of behaviors characterize compulsive buying: impulsive buying and obsessive-compulsive buying (Ridgeway et al, 2008). Impulsive buying refers to unplanned purchase due to consumers’ inevitable impulse (Kacen & Lee, 2002), and obsessive-compulsive buying reflects a preoccupation in buying to reduce anxiety (Ridgway et al, 2008). Compulsive buying behaviors have been analyzed under the framework of motivation theory (McGuire, 1976). Nevertheless, research is still needed to understand more on the phenomena of compulsive buying (e.g., Kukkar-Kinney et al., 2016; Japutra et al., 2017). This study aims to explore the antecedents of compulsive buying behaviors using implicit theories. According to the implicit theories, mindset shapes the motivation of consumer behaviors (Dweck, 2000; Murphy & Dweck, 2016). Mindset refers to the beliefs about the nature of human characteristics, and individuals may possess two types of mindset – fixed and growth mindset (Murphy & Dweck, 2016). People with a fixed mindset believe that individuals’ qualities such as intelligence and competence are set and hard to change, whereas those with a growth mindset tend to believe that all individuals are able to change and develop through efforts and experiences. We argue that mindsets influence compulsive buying, and we propose that deal proneness mediates the relationship between mindset and compulsive buying. In doing so, we aim to enhance our knowledge in understanding how mindset affects compulsive buying behavior. Literature review and hypotheses According to the implicit theories, consumers with fixed mindsets believe that one’s abilities are fixed and hard to improve, and thus feel the need to prove, to themselves and others, that they have the abilities and/or they are successful (Murphy & Dweck, 2016). Thus, fixed mindset consumers tend to demonstrate their worth by using image-enhancing products and brands (Park & John, 2012). Deals, such as free gifts and offer of coupons, reduce the transaction cost and increase the perceived value of these image-enhancing purchases. In particular, since fixed mindset consumers favor success with little effort (Murphy and Dweck, 2016), deals can help them achieve their goal of image enhancement with lower cost. Thus, we hypothesize that: H1 Fixed mindset is positively related to deal proneness. For consumers with growth mindsets, a major motivation for their consumer behavior is to learn and improve (Murphy & Dweck, 2016). As the research by Blackwell et al. (2007) shows, growth mindset consumers are eager to participate in the self-improving process and achieve mastery. Thus, growth mindset consumers may perceive the information of discounts, free gifts and coupons as part of the adventurous process where they make the cost-benefit analysis and improve their abilities as wiser consumers. Thus, we propose: H2 Growth mindset is positively related to deal proneness. Previous studies show that compulsive buying is associated with high deal proneness (Kukar-Kinney et al, 2012). Deals may imply perceived value of the purchase and enhanced shopping enjoyment (Grewal, Monroe, & Krishnan, 1998), and thus serve as an effective contextual factor in inducing compulsive behaviors (Kukar-Kinney et al, 2016). Furthermore, deals provide an excuse and rationale for the purchase, which can be used to overcome the sense of guilt compulsive buyers often experience after their compulsive buying behavior (O’Guinn & Faber, 1989). Thus, we make the following hypotheses. H3 Deal proneness is positively related to impulsive buying. H4 Deal proneness is positively related to obsessive-compulsive buying. Finally, we argue that deal proneness mediates the relationship between consumer mindsets and compulsive buying behavior. According to the implicit theories, consumer mindsets inspire how consumption goals are pursued (Murphy & Dweck, 2016). Consumers with a fixed mindset pursue a performance goal, and they tend to use brands to feel positive about themselves and improve impression on others (Park & John, 2010). In contrast, consumers with a growth mindset hold that people can always learn and improve and thus are tuned to learning goals (Murphy & Dweck, 2016). Hence, for fixed mindset consumers, deal offers suggest lower costs for image-enhancing purchases, and provide an excuse for the compulsive buying behavior. For growth mindset consumers, deal offers can imply a learning and adventurous process .These consumers may feel that they can make better purchasing decisions by taking advantage of various deals. We thus hypothesize that: H5 Deal proneness mediates the relationship between fixed mindset and impulsive buying (H5a), between fixed mindset and obsessive-compulsive buying (H5b), between growth mindset and impulsive buying (H5c), and between growth mindset and obsessive-compulsive buying (H5d). Method A questionnaire was developed to gather responses and test the hypotheses. All of the items to measure the constructs were developed from existing scales based on previous research. Fixed and growth mindsets were measured using scales developed by Park and John (2012). Deal proneness was measured using items following Lichtenstein et al. (1997). Impulsive buying and obsessive-compulsive buying were measured using items developed by Ridgway et al. (2008). All items were rated on a 7-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (7). The questionnaire was administered using an online survey (N=421 respondents). Of these, 57.7% were female, 71.5% had a university degree, 50% were 31-40 years old, 41% were 26-30 years old, and 46.3% had a monthly income of 5,001-10,000 RMB. Results and discussion To test the hypotheses within the research model, a Structural Equation Modeling (SEM) approach was employed, using AMOS 18.0. First, a measurement model was created to assess the validity and reliability of the scales. The distribution of the data was checked. The absolute value of the skewness and kurtosis of each items were within +/- 1, suggesting normal distribution was achieved. The measurement model produced good fit (Hair et al., 2010): χ2(109) = 281.21, χ2/df = 2.58, GFI = .93, NFI = .93, CFI = .96, and RMSEA = .06. All values representing the AVE were greater than 0.5 and greater than the squared inter-constructs correlations, indicating convergent and discriminant validity were achieved (Fornell & Larcker, 1981). Cronbach’s alpha values exceeded .70, indicating the constructs were reliable (Hair et al., 1995). The results of the checking common-method variance problem through exploratory factor analysis (EFA) test revealed 3 factors with Eigen values greater than 1. The results accounted for 64.67% of the total variance, where the first factor accounted for 27.55% of the total variance, suggesting that common-method variance did not pose a significant problem since there was no general factor in the un-rotated structure (Podsakoff et al., 2003). Next, a structural model was created. The structural model produced good fit (Hair et al., 2010): χ2(114) = 476.15, χ2/df = 4.18, GFI = .89, NFI = .89, CFI = .91, and RMSEA = .09. Table 1 displays the results of SEM. The results support H1 and H2. Both fixed and growth mindsets are positively associated with deal proneness. The results support H3 and H4, which shows that deal proneness are positively associated with impulsive and obsessive-compulsive buying. The results support H5a, which states that deal proneness mediates the relationship between fixed mindset and impulsive buying. However, the results do not support H5b, H5c and H5d. Conclusion Using the implicit theories, this research aims to gain better insight into compulsive buying behavior. Our findings, obtained from a sample of respondents in China, show that deal proneness serves as a mediator between fixed mindset and compulsive buying behaviors. According to the implicit theories, consumer mindsets inspire how consumption goals are pursued (Murphy & Dweck, 2016). For instance, consumers with a fixed mindset pursue a performance goal. They tend to use brands to feel positive about themselves and improve impression on others (Park & John, 2010). Thus, it is likely that fixed mindset consumers buy compulsively to signal and communicate their “self” to others. In particular, for fixed mindset consumers, deals may increase the perceived value of image-enhancing purchases. Thus, deals provide an excuse for the compulsive purchase where fixed mindset consumers can improve self-image and demonstrate their worth with lower costs. Given this mediating role of deal proneness between fixed mindset and compulsive buying, it will be interesting to test further how consumers with a fixed mindset respond to different types of deals in future research. For firm managers and public policy makers, our findings imply that, to lessen consumers’ overspending, firms should reduce excessive number of deals, and governments should also regulate firms’ advertisement so that it will not overly promote deals.

Introduction Credit card issuers across countries now offer automated payment facilities online to ensure that consumers commit to regular repayments. However, insofar it is unclear whether repayment automation leads to better financial decisions. With an average of $880 billion of revolving debt in the U.S., it is no surprise that policy developers seek to remedy the global credit card debt problem. The current research makes three contributions. First, our study raises public awareness about the negative effects of automated payments on credit card repayments. Contrary to the established assumptions that autopay helps consumers to manage consumer finances (e.g., www.directdebit.co.uk), our experiment unanimously show that autopay facilities reduce the amount of credit card repayment. Second, our study offers a contemporary and relevant insight into the consumers’ online credit card management, which is distinct from its offline counterpart. Specifically, in an online environment, consumers can process information on their credit card and saving almost simultaneously. For example, some consumers may access credit and saving accounts in different browser tabs, while others who own credit and saving accounts from the same institutions may be able to access both accounts within the same webpage. Finally, our study enriches understanding of individual differences in repayment decisions behaviour. Our results indicate that certain attitudinal tendencies to credit cards heightens the effect of autopay on repayment, but this effect is intensified when the context involves those with low level of saving. Conceptual Development The Psychology of Automated Payment Credit cardholders often set up automatic monthly payments to avoid missed payments and incur penalties. The freedom and convenience associated with online banking means that credit card consumers can easily set up automated payment at an amount that they feel comfortable. Consumers can choose any amount ranging from the minimum amount, which is typically is set at 2% of the overall balance, to the full credit card balance. Prior research on goal pursuits suggests that people divide goals into subtasks to experience the motivational benefits of greater self-efficacy (Bandura, 1997). In this case, the use of automated repayment provides a sense of goal progress as it allows repayments to be made in smaller instalments, which in turn, bolsters one’s perception of self-efficacy with respect to the overall goal (i.e. total credit card balance). However, a boosted sense of achievement resulting from subgoal completion may lead consumers to undermine absolute progress towards the overall goal. As such, the subgoal – rather than the superordinate goal – becomes the most salient point of reference for individuals’ motivations towards goal pursuit (Besharat, Carrillat, & Ladik, 2014). Unfortunately, the focus on subgoals can lead to a sense of complacency and reduced persistence towards superordinate goal (Gal & McShane, 2012). Therefore, we expect that the presence of automated repayment cause consumers to focus on the more manageable subgoals (i.e., monthly repayments) rather than the unwieldy superordinate goals (i.e., total credit card balance). In addition, we theorise that the convenience of automated payment removes the salience of the “pain of paying” (Prelec & Loewenstein, 1998) away from future credit card repayment. A key characteristic of credit card expenses is that the “pain” of payment, which provides a nudge for self-reflection and intervention from overspending, is held at bay until the end of the month. However, with automated payment, such deliberation point is subverted to a one-time deliberation. Because automated payment shifts attention away from subordinate goals and reduces the complexity of monthly deliberation, we expect that consumers making automated credit card repayments will commit to less amount of repayment than those making non-automated payments. H1: Automated payment leads to lower repayment amount compared to regular non-automated payment. The Psychology of Credit and Saving Accounts The default setup of many credit card accounts tends to demarcate credit and debit (saving) accounts. For example, consumers may have separate login accounts to access information about their credit and debit accounts. Such financial accounts separation means that consumers also categorise debt and saving into separate mental accounts (Hershfield, Sussman, O’Brien, & Bryan, 2015). Previous research suggests that such erroneous categorization of overall wealth can lead consumers to make financially detrimental decisions, such as taking on high-interest rate debt, while simultaneously holding money in low-interest rate saving account (Sussman & O’Brien, 2015). The absence of overall wealth information in credit card accounts and statements means that people are likely to focus their attention to arbitrary information that may misshape one’s perception of wealth. We therefore expect that the absence of accurate information of financial capability in the form of saving account balance will lead consumers to anchor their repayment decisions on perceived wealth informed by the available credit limit. In contrast, the presence of saving information in credit card account has a direct influence over credit card repayment decision because it represents an accurate picture of one’s overall wealth. Thus, higher (lower) balance of saving account will lead to higher (lower) credit card repayment. We expect that the positive effect of credit and saving account on repayment transcend over the effect of repayment mode (i.e. automated versus non-automated repayment) as it reconciles the consumers’ saving and debt mental accounts. Hence: H2: The amount in saving account influences the amount of credit card repayment. Individual Differences in Susceptibility to Credit Card Debts Prior studies regularly report that credit card as a payment mechanism yield psychological effect on the consumers’ evaluation at the point of purchase. In comparison to more transparent and vivid payment methods such as cash, credit card payments causes consumers to trivialise past payment (Soman, 2001), reduces self-control (Chatterjee & Rose, 2012) and overvalue past income (Soman & Cheema, 2002). However, other studies suggest that consumers exhibit different individual differences in susceptibility to credit card’s psychological effects (Awanis & Cui, 2014; Rick, Cryder, & Loewenstein, 2008). For example, those characterised as spendthrifts, instant gratifiers, low in self-regulation and financial sophistication are likely to emphasise on the bright side of credit cards (i.e., spending/lifestyle facilitator). Consequently, these consumers tend to overspend with their credit cards. We expect that such individual differences in credit card mentality will reflect on the consumers’ repayment habits. Thus, we expect a negative relationship between individual-level susceptibility to credit card debts and repayment amounts. In addition, we also expect that individual differences in credit card debts susceptibility will moderate the relationship between automated payment and repayment decision (H1). Indeed, those who advocate the bright side of credit card (high susceptibility) may appreciate, or even celebrate automated payment facilities, as it makes credit card experience more convenient and worry-free. To this end, we suggest that individual-level differences in susceptibility to credit card debts will moderate the relationship between automated payment and repayment amounts. Furthermore, we propose that such moderated relationship is stronger and consequently more problematic among those with constrained resources (low saving). Specifically, cash-strapped consumers are at risk of placing greater emphasis on the bright side of credit cards to make up for their lack of financial resources. The combined effects of individual susceptibility to credit card effects and the misguided promise of automated payment are likely to lead these individuals to a path of revolving debt. Meanwhile, those with sufficient resources are unlikely to suffer the same extent of indebtedness due to their wealth. Thus, we hypothesise that the moderating effect of individual susceptibility to credit card debts on the relationship between automated payment and repayment amount will differ across those with low and high saving: H3: In low saving conditions, susceptibility to credit card debts moderates the relationship between automated payment and the amount of credit card repayment; in high saving no such moderation effect is expected. Method We conducted a 5 (current account balance) x 2 (payment mode) between-subject experiment involving a hypothetical scenario and repayment decisions. Current account balance has five levels: no account balance information (served as a control condition), $500, $1000, $2000 and $3000 and payment has two levels: autopay and regular payment. Across all experimental conditions, the minimum required payment and credit card balance were kept constant. In total, eight hundreds and nine US credit card users (458 women, 11% were aged 18-44 years, 42% from 25-34 years, 24% from 35-44 years and 23% were aged more than 45 years) were drawn from Amazon Mechanical Turk and were paid $.35 each for participation. Participants were asked to imagine that they had just logged onto their online account where they could see their online credit card statement with a balance of $1.937.28 and a minimum payment of £35.78. This minimum required payment was equal to two-percent of balance. The amount of credit card balance reflects the U.S. average of consumer credit card balance (Salisbury, 2014). Participants were told that they also saw their current accounts (i.e., the amount of money in their debit cards) and were also told that they do not have any other forms of financial obligations. Participants were instructed to indicate the amount of credit card repayment they would make in the light of the information provided in the online statement. We expect that the consumers’ understanding of compounding interest will affect their credit card repayment decisions. Therefore, we controlled for the participants’ financial knowledge, measured using three quiz-style questions following Navarro-Martinez, et al. (2011). Scores were calculated by tabulating the number of correct answers (one score for a right answer and zero for a wrong answer) and points are summed across the three questions to arrive at a single knowledge score. We measure participants’ susceptibility to credit cards effect (SCCE) by a 12-items scale adapted from Awanis and Cui (2014) (Cronbach’s α=0.89). The scale has been found to be invariant across cultures e.g., UK and Singapore. The scale items used a 7-point Likert format (1=strongly disagree, 7=strongly agree). Results and discussions A 5 (account balance) x 2 (payment mode) ANOVA revealed a main effect of current account balance, F(4,755)=61.50, p<0.001, η2=.246, such that higher current account will lead to higher repayment (Mcontrol=$960.64 (SD=53.91), M1=$181.85 (SD=58.08), M2=$390.55 (SD=53.78), M3=$1075.07 (SD=54.71), M4=$1138.45 (SD=51.84), see Figure 1). The ANOVA design also revealed a main effect of autopay vs regular payment mode, F(1,755)=28.44, p<0.001, η2=.04, such that the autopay (Mautopay=619.39, SD=35.17) brought about lower payments than the regular mode (MRegular=879.24, SD=33.72). Therefore, H1 and H2 are supported. Interaction effect of payment mode and susceptibility within low versus high account balance. We then examined the interaction effect of payment mode and susceptibility to credit card debt within three conditions: account balance is lower than the credit balance (high saving) and account balance is higher than the credit balance, and a control condition. We, therefore, recode the five levels of account balance experimental conditions into a dummy variable with three levels: 0 for control, 1 for low account balance and 2 for high account balance. The experimental conditions with account balance lower than the credit balance (i.e., $500 and $1000) is coded as 1 and those with account balance higher than the credit balance is coded as 2, no account balance information presented (i.e., control condition) is coded as 0. We centred the means of SCCE and use a PROCESS macro (Hayes, 2013) to estimate the interaction effect. Within low balance: the moderated regression results revealed the main effect of autopay (b=-233.23, t=-7.46, p=<0.001), main effect of SCCE (b=-43.318, t=13.04, p=<0.001) and interaction effect between autopay and SCCE (b=-81.48, t=25.64, p=<0.001) on credit card repayment. Simple slope analysis reveals that at there were significant differences in the repayment amount between low vs. high SCCE for regular participants (b=-81.70, t==-5.55, p<0.001). In contrast, for autopay participants, the effect of SCCE on credit card repayment is not significant (b=-.21, t=-.01, n.s). Within high balance: the moderated regression results revealed the main effect of autopay (b=-317.39, t=-3.57, p=<0.001) and main effect of SCCE (b=-135.10, t=-3.88, p=<0.001) on credit card repayment. The interaction effect between autopay and SCCE on credit card repayment is not significant p>.5).Within control: the moderated regression results revealed the main effect of autopay (b=-301.69, t=-2.13, p=<0.001) and main effect of SCCE (b=-156.24, t=-2.58, p=<0.001). The interaction effect between autopay and SCCE is not significant p>.5). Figure 2 shows the interaction effect discussed above for the two account balance experimental conditions: low account balance (panel A), high account balance (panel B). Patterns in control condition is similar to panel B. Based on these results, H3 is supported. General Discussion Automated payment is not as virtuous as many have assumed. In fact, autopay facilities encourage may reduce consumers’ long-term goal of debt repayment by craftily shifting attention away from superordinate goals to the more manageable and rewarding subgoals. We recommend that policy developers and practitioners should exercise caution in promoting the use of automated payment to enhance financial management. Such recommendations should come with a set of actionable guides to reduce debt levels in shorter time. Our findings also suggest that separation of many credit and debit accounts means that people tend to categorize debt and saving into separate mental accounts. This affects people’s ability to make informed repayment decisions, which should reflect one’s real ability to pay. Interventions that help people to accurately measure their real financial capabilities are expected to raise their repayment decisions. Therefore, we suggest that policy makers and practitioners reconcile credit card and saving account in a single online platform to enhance the consumers’ repayment decision.

This research examines how consumer ethnocentrism and global social bonding affect consumer appreciation for foreign products in the home country. Our research findings show that consumer ethnocentrism lowers diversity appreciation; global social bonding enhances diversity appreciation; and global social bonding moderates the relationship between consumer ethnocentrism and diversity appreciation.

Consumer ethnocentrism (CET) has been widely research in various marketing contexts since the construct was identified by Shimp and Sharma (1987) in their seminal paper. The central tenet of consumer ethnocentrism theory is that consumer ethnocentrism will have a negative effect on foreign product purchase intention and a positive effect on willingness to purchase home country products. So far, the role of satisfaction has not been integrated into the CET model. The satisfaction-repurchase relationship has also received considerable attention in the marketing literature whereby satisfaction is found to have a direct positive effect on repeat purchase. Anecdotal evidence suggests that both satisfaction and consumer ethnocentrism will have a joint effect on willingness to repurchase a home country product, especially when foreign competitor products are seen as a threat in the domestic markets. However, it remains unclear how satisfaction and consumer ethnocentrism jointly affect purchase intentions. In this research we examine the dynamics of the two constructs. This study considers South Korean consumers’ willingness to repurchase the Samsung Galaxy smartphone and examine the interrelationship among the above variables. The findings of the study suggest that consumer ethnocentrism moderates the satisfaction-repurchase intention relationship or vice versa and satisfaction and consumer ethnocentrism are mutual cooperative suppressors for repurchase intention. This study highlights that the effect of consumer ethnocentrism on repurchase intention will be stronger when consumers are satisfied with the product.

Online shopping is significantly increasing worldwide and is showing a continuous potential in terms of growth, security, price and shopping convenience. It was noticed that 65% of online shoppers browse for products online every day and this activity has become customer’s part of their everyday life, which is an advantage for second hand online retailers (Wares, 2012). The trend emerging for purchasing second hand goods from online retailers has also shown an increase recently (Sharman, 2012). Second hand online shopping is one of the industries that everlastingly remain unaffected even if it undergoes any economic circumstances (Heller, 2011). USA and UK are the leading countries of second hand online shopping with most sales being incurred from early years. Considering the newly advanced BRIC countries, both China and Brazil are famous for second hand online shopping and are expecting to undergo a constant increase in the near future. Russia and India had a very slow growth of second hand online shopping from the year 2004 and remained slow, but increased during recession and will incure higher sales by early 2014 (Rueter, 2013). Thus, the question arises - what makes second hand goods sold online from unknown online retailers valuable? The answer might be found in the notion of perceived value that has become an important construct within the e-business framework because of such an easy access to online retailers. If customers perceive the value of second hand goods sold by the unknown online seller as high, they are more likely to enter into the transaction with that retailer. The company’s reputation also plays an important role here as it represents an asset to the owner and customers would trust the seller because of that asset (Kirmani and Rao, 2000). This asset turns out to be even more important when it becomes hard to evaluate the seller in cases of purchasing services or when shopping online. This study examines effects of antecedents on perceived value of second-hand goods sold online. Specifically, the study uses the data from an online survey collected across various countries. The data suggests significant effects of antecedents on perceived value of goods sold by second-hand online retailers.