From Mars to Venus: Alteration of trust and reputation in online shopping

Abstract

How customers develop purchase intentions in online shopping has been an area of recent interest. In this study, we ask whether males and females leverage trust into and perceived reputation of online retailers differently in reporting purchase intentions. Drawing on the literature on differences in males and females in online shopping behaviors, we propose that females rely on reputation in developing purchase intentions whereas males rely on perceived trust in developing purchase intentions. In a sample of 727 respondents in Sweden, we find support for the proposed associations. Implications for online retailers are discussed.

Introduction

The worldwide electronic commerce sales reached $3.46 trillion in 2019.¹ Due to the limited tactile interaction with the products in online purchases, there are several logistical and transactional challenges. While 30% of the products purchased online are returned, an additional challenge is developing and maintaining ongoing purchase intentions through improved customer acquisition and retention costs as repeat purchases are declining and repeat customer acquisition costs are increasing (Fernie & Sparks, 2014). With online purchases increasing at a rapid pace and with the broader social acceptance of buying a variety of goods online (e.g., mattresses), there is a growing practitioner interest in increasing repeat purchase behavior. Accordingly, developing purchase intentions are shown to be a precursor to inducing repeat purchases.

In this regard, both trust and reputation of online retailers are lubricants to online purchases (Chiu, Wang, Fang, & Huang, 2014). Trust and reputation could have different perceptions between the sexes. With both trust and reputation gaining increasing relevance in research how online retailers manage the two is important to understand. Our premise is based on a substantial body of research that has found evidence that female and male respond to and express risk differently (e.g., Simon, Houghton, & Aquino, 2000; Brindley, 2005; Eckel & Grossman, 2008; Abubakar, Ilkan, Al-Tal, & Eluwole, 2017).

Males and females could have different perceptions of trust and reputation in developing repeat purchase behaviors. Trust and reputation address uncertainty in retail purchase through two different channels. Trust, through integrity, benevolence, and ability (Gefen, 2002) could be more valued by males, who are more likely to trust than females (Kolsaker & Payne, 2002; Riedl, Hubert, & Kenning, 2010; Van Slyke, Comunale, & Belanger, 2002). Whereas reputation lowers concerns for opportunism and enhances perceptions of status, that could be preferred by females looking to lower dissonance and risk and seek higher status (Cyr, Hassanein, Head, & Ivanov, 2007). Based on these rationales we propose the contingency that females and males are more likely to report higher purchase intentions when perceived reputation and trust, respectively, is higher in the online retailer.

Hence, given these challenges, and building on prior work on how reputation and trust influence purchase intentions (Aguirre, Mahr, Grewal, de Ruyter, & Wetzels, 2015; Kim & Peterson, 2017; Oghazi, Karlsson, Hellström, & Hjort, 2018), we ask whether females and males leverage reputation and trust differently in developing purchase intentions. Our primary research questions are as follows—are females more likely to develop repeat purchase intentions when they perceive more positive reputation of online retailers? Are males more likely to develop repeat purchase intentions when they perceive trust in online retailers? Drawing on the broader literature on differences in perceived risk-taking and potential cognitive dissonance between male and female customers (Chen, Yan, Fan, & Gordon, 2015; Chiu et al., 2014; Roghanizad & Neufeld, 2015), the proposed research questions aim to contribute to the online retailing literature and have implications for online retailers.

The structure of this paper is as follows. We first review the literature on reputation and trust in the online retailing literature and the differences in males and females in developing purchase intentions. Thereafter we discuss the research method, followed by the presentation of our analyses and findings. Finally, we provide a discussion of our results and the associated implications. We conclude with limitations of our study and potential future research directions.

Theoretical development and hypotheses

In making online purchase decisions, assessing quality, fit, and desirability, at times, are difficult to ascertain. The distance between the customer and the product and temporal gap between purchase and receiving the product creates purchase uncertainty. This uncertainty has multiplied the need for trust and reputation in the online retailer. The extent to which the customers perceive that the transaction will be completed as promised and the general reputation that would help manage exceptions are essential elements of building relationships in an online environment.

Past studies have shown systematic differences between male and female customer behavior in online purchases. Studies have shown that males are generally more intensive users of e-commerce platforms (Shiu & Dawson, 2004; Verhagen & van Dolen, 2011) however, the gap has narrowed significantly in the past decade. According to a recent statistic on weekly frequency of online purchase, 30% of the male are online buyers compared to 18% of female online buyers. Rooted in the technology adoption models, males and females differ systematically in use of technology. Building these differences in technology adoption, Sun, Lim, Jiang, Peng, and Chen (2010)) propose that differences in biological, cognitive, and social factors could influence online purchase behaviors. Gender differences in online shopping have been explored the differences between risk in the online purchase (Walsh, Schaarschmidt, & Ivens, 2017), e-commerce usability and design (Hjort, Hellström, Karlsson, & Oghazi, 2019; Jones & Kim, 2010) and technology acceptance (Koufaris, 2002; Vakulenko, Oghazi, & Hellström, 2019).

According to Zhou, Dai, and Zhang (2007)) and Hasan (2010), females differ systematically from males in their online shopping preferences. Compared to males who prefer the convenience of shopping, the lack of face-to-face interactions and experiences may lower participation of females in online shopping. Females prefer to touch or feel the products. Generally, the products available on e-commerce websites are male-oriented, whereas female-oriented products (e.g., cosmetics) are may not be available online through an experience that would allow for the online purchase of such products.

Females, online retailer trust, and purchase intentions

Reputation is a lubricant for online transactions (Chiu et al., 2014). Reputation allows for lower transaction costs, lower risk, and also a barriers to entry (Akroush & Al-Debei, 2015). Given the multifaceted risks in online purchases, reputation helps develop a credible bond necessary to strengthen trustworthiness, improve integrity in exchanges and promote reliability in ongoing transactions (Hsu, Chuang, & Hsu, 2014). Reputation aims to lower the behavioural uncertainty in online exchanges and signals the implicit quality of an online retailer. As stated in the study of Eisenbeiss, Cornelißen, Backhaus, and Hoyer (2014)), ‘firm reputation can also serve as an important heuristic cue in post-purchase situations’ (page 257). Recent works, drawing on signalling theory have highlighted the value of reputation in online exchanges wherein customers lacking information on an online retailer use reputational cues to develop beliefs and drive purchase intentions (Kozlenkova, Palmatier, Fang, Xiao, & Huang, 2017; Li, Fang, Wang, Lim, & Liang, 2015; Oghazi et al., 2018). Reputation reduces perceived risk, signals higher quality, and provides an ex-ante assurance of a reliable exchange (Fang et al., 2014).

Studies have shown that gender-related differences influence how signals are perceived. Females perceive higher product related risk and greater cognitive dissonance (Liang, 2016). As such, reputation as a signal may be salient for females as it reduces the likelihood of negative decision outcomes and reduces risky cues associated with online purchases (Kim, Xu, & Koh, 2004). More receptive to risk-reducing cues, females are likely to draw on reputation in making online purchase decisions. In addition to risk and cognitive dissonance assuaging advantages of reputation, reputation also allows improved self-concept and fashionableness (Hsu et al., 2014; Lu, 2017; Mousavizadeh, Kim, & Chen, 2016). Females are also more responsive to negative reputation cues (Dawson & Kim, 2009). Because reputed online retailers are more likely to have better quality and fashionable choices, females may rely more on reputation in reporting higher online purchase intentions than men. Hypothesis 1

With enhanced reputation of the online retailer, females are likely to have higher purchase intentions than males.

Males, online retailer trust, and purchase intentions

Trust in the online e-commerce website has also been an important area of research. Online trust refers to the to which customers typically trust the online business activities of a firm. Central to online trust are three components -- ability, benevolence, and integrity (Lee & Turban, 2001). The degree to which online purchase tasks are performed reliably, with benevolent motives (e.g., generous returns policy) and with fairness and honesty (integrity) are the key elements of online trust. Trust in online settings is generally a path-dependent process where customers after engaging in multiple transactions are likely to develop their perception of trust. Alternatively, for more established retailers, online reviews from other customers could form the basis of trust. Trust is elemental in providing assurance of an honest and transparent purchase transaction and assures privacy and security (Chen & Dibb, 2010; Mukherjee & Nath, 2007). Trust signals commitment and assures fair governance of online purchase.

A recent meta-analysis (Kim & Peterson, 2017), identified about 16 antecedents to online trust, ranging from a disposition to trust to perceived risk and from perceived design quality to perceived information quality. These antecedents range from individual-level dispositions to perceptions of presented online environment or information. Research in business has demonstrated that female have greater trust concerns than male and are less likely to engage in purchasing over the web due to trust issues (Buchan, Croson, & Solnick, 2008; Sheehan, 1999).

Research in social psychology shows that males are more likely to trust online shopping than females (Van Slyke et al., 2002). Males demonstrate a higher level of trust in online shopping than females (Cyr & Bonanni, 2005), however, the differences in trust towards online purchase between sex remains mixed (Kim & Peterson, 2017). A variety of studies in economics confirm that women are more cautious when faced with uncertainty and risk (Borghans, Heckman, Golsteyn, & Meijers, 2009). The negative risk–trust link is more negative for women than men because of report higher risk proneness that lower trust in transaction partners (Dillon, Buchanan, & Al-Otaibi, 2014; Garbarino & Strahilevitz, 2004). As such, females are likely to develop trust at a lower rate than males, resulting in males developing stronger purchase intentions at increasing levels of trust. A study by Van Slyke et al. (2002) shows that compared to males, female customers are more sensitive to risk and are more likely to be rational than males. Females are also more likely to be cautious and conservative in developing purchase intentions (Bae & Lee, 2011).

Based on the above discussion, we expect that males are likely to have stronger purchase intentions when perceiving a higher level of trust. Hypothesis 2

With increasing trust in the online retailer, males are likely to have higher purchase intentions than females.

Research methods

Sampling and data collection

To test for the proposed hypotheses, we conducted an online survey of students at a major university in Sweden. The data used in this study was used earlier in Oghazi et al. (2018), and we have controlled for the variables in that study to avoid having current findings driven by an artifact of specification bias. The university, where the data was gathered, has approximately a student population of 30,000, and the sampling frame is consistent with the studies using student samples in research on online purchases. Furthermore, the sampling pool is demographically salient in the sense that students are generally active users of online purchase platforms and are more computer literate. Our sample is consistent with recent studies drawing on student samples to study online purchase behavior (e.g., Shaouf, Lü, & Li, 2016; Verhagen & Bloemers, 2018).

An online survey was sent to 4890 potential respondents. In responding to the survey, the respondents were asked to focus on their use of online purchase platforms in responding to the survey questions. Of the 4890 potential respondents, a total of 730 surveys with complete responses were received. The response rate of 14.92% is comparable to typical response rates in survey research. After case-wise deletion, our final sample consisted of 727 responses. In the sample, a majority of the respondents are male online consumers (61.5%) between 18 and 34 years (97.7%). As Table 1 shows, 80.10% of the respondents have received higher education, while 60.1% have high computer skills with 71.2% having an annual income less than SEK 150, 000. Though the reporting on high computer skills is substantially high, it seems to be a meaningful considering the student sample.

Table 1.

Sample descriptive.

	Variable	N	Mean	SD	Min	Max	1	2	3	4	5	6	7	8
1	Purchase intention	727	5.7549	1.2879	1	7	0.91
2	Reputation	727	5.3821	1.2518	1	7	0.6646*	0.83
3	Perceived trust	727	4.6383	1.0265	1.3333	7	0.5604*	0.6917*	0.61
4	Gender (2=Female; 3= Male)	727	2.6149	0.4870	2	3	−0.0134	0.0028	0.0355	1
5	Annual income	727	11.8047	4.0165	2	19	0.0911*	0.0801*	0.0976*	0.0319	1
6	Age	727	6.5585	1.0726	2	11	0.0042	−0.0771*	−0.0407	0.1196*	−0.1962*	1
7	Education	727	3.4869	1.3604	2	9	−0.1027*	−0.0788*	−0.0193	0.0069	−0.0622	0.2334*	1
8	Computer Skills	727	4.2270	1.1862	2	7	0.0552	−0.0063	0.0122	0.0919*	0.007	0.0637	−0.0097	1
9	Online purchase frequency	727	3.4085	1.2385	2	6	−0.0796*	−0.0415	−0.0278	0.0329	0.1152*	−0.0289	−0.034	−0.0444

Note. *p < 0.05 (two-tailed); the value in the diagonal are Cronbach’s alpha.

Measures

The outcome variable purchase intention was evaluated using a three-item seven-point scale (Pei, Paswan, & Yan, 2014): I would be willing to purchase at this store; It is very likely that I would make a purchase from this store; The probability that I would make a purchase from this store is very high. The items reflect the willingness, likelihood and probability of making a future purchase.

Reputation was measured using a four-item scale seven-point scale from Pei et al. (2014) and Teo and Liu (2007): The retailer has a good reputation in the market; The retailer has a reputation for being honest; The retailer has a reputation for being fair; The retailer has a reputation for being consumer oriented. The perceptions of reputation are reflected in the perceptions of good reputation in the online retail space, the general perception of retailers honest in their tranactions and being fair. Customer orientation is also an important indicator of reputation.

Trust in the online site was measured using six-item seven-point scale from Hsieh (2013): The store cannot be trusted at times (reversed coded); The store can be counted on; The store has high integrity; The store is honest; I have confidence in the store; I am willing to let the store make important decisions without my involvement. The elements of the trust items refer to the perceived trust in the online portal, the extent to which the online portal close gaps in perceived uncertainty from buying from the portal. Generally, this scale refers to perceived honesty, confidence and perceived willingness of the portal to make informed decisions for the customer. The measure taps into the multifaceted nature of trust in online transactions.

Gender was measured as a categorical variable.

To lower the influence of competing explanations for our findings, we included several controls. Because income levels could influence purchase intentions, we included a measure of annual income (1: 1500001−250000Kr; 2: 150001−250000Kr; 3: 150001−250000Kr; 4: 150001−250000Kr; 5: 250001−350000Kr; 6: 250001−350000Kr; 7: 250001−35000Kr; 8: 350001–450000 Kr; 9: 350001–450000Kr; 350001−45000Kr; 10: 50000–150000 Kr; 50000–150000Kr; 11: Less than 50000 Kr; 12: Less than 50000Kr; 13: Less than 50000Kr; 14: More than 450000 Kr; 15: More than 450000Kr).

Next, we controlled for age as a categorical variable (1: 17 or younger; 2: 18–25; 3: 26–34; 4: 35–49; 5: 50 or older) because generational differences could explain differences in propensity to purchase online. We also controlled for education level (1: High school or less; 2: Bachelor degree; 3: Master's degree; 4: Ph.D.). Because computer skills could directly influence the use of online purchase platforms, we controlled for the self-reported computer skills (1: very basic; 2: basic; 3: intermediate; 4: advanced; 5: very advanced). We also controlled for online purchase frequency (1: Once a year or less; 2: Once a season; 3: Twice a season; 4: Once a month or more).

Data analysis and results

Table 1 lists the sample descriptive. The Cronbach’s alphas for the control variables to evaluate reliability is shown in the diagonal in Table 1. Purchase intention is positively associated with reputation (r = 0.66) and perceived trust (r = 0.56). Reputation and perceived trust were positively related (r = 0.69). Those with higher income had higher purchase intentions (r = 0.09), however, those with higher education (r = -0.10) or higher online purchase frequency (r = -0.08)

had lower purchase intentions.

Table 2 presents the estimates of OLS regression. In model 1 we introduce the controls. Both reputation and trust had a positive and significant association with purchase intention. Similarly, annual income, age, and computer skills were associated with higher purchase intentions. Consistent with the correlation table, education and online purchase frequency were negatively associated with purchase frequency.

Table 2.

OLS estimates.

		(1)	(2)	(3)	(4)
Variables	Hypothesis	Purchase intention	Purchase intention	Purchase intention	Purchase intention
Reputation X Gender	[H1]		0.191***		0.151*
			(0.0561)		(0.0771)
Perceived trust X Gender	[H2]			0.197***	0.0702
				(0.0686)	(0.0941)
Gender		−0.0927	−1.116***	−1.002***	−1.229***
		(0.0724)	(0.310)	(0.325)	(0.345)
Reputation		0.541***	0.0508	0.537***	0.151
		(0.0387)	(0.149)	(0.0385)	(0.201)
Perceived trust		0.242***	0.238***	−0.263	0.0584
		(0.0471)	(0.0467)	(0.182)	(0.245)
Annual income		0.0154*	0.0150*	0.0164*	0.0155*
		(0.00893)	(0.00887)	(0.00889)	(0.00889)
Age		0.0943***	0.0926***	0.0945***	0.0930***
		(0.0343)	(0.0341)	(0.0341)	(0.0341)
Education		−0.0701***	−0.0652**	−0.0646**	−0.0643**
		(0.0264)	(0.0262)	(0.0263)	(0.0263)
Computer Skills		0.0553*	0.0536*	0.0544*	0.0537*
		(0.0295)	(0.0293)	(0.0294)	(0.0293)
Online purchase frequency		−0.0570**	−0.0590**	−0.0599**	−0.0596**
		(0.0283)	(0.0281)	(0.0282)	(0.0282)
Constant		1.368***	4.033***	3.701***	4.314***
		(0.374)	(0.868)	(0.895)	(0.946)
Observations		727	727	727	727
R-squared		0.477	0.486	0.483	0.486
F-stat		81.98	75.23	74.52	67.72
R-sqaure		0.477	0.486	0.483	0.486

Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

In model 2, testing for H1, with increasing reputation females are likely to report higher purchase intentions (β = 0.191, p <  0.01). This inference is confirmed in Fig. 1(a). The estimate of the interaction term in model 4 is consistent with the one in model 2.

Fig. 1.

Moderation plots.

(a). Moderation effect of gender on reputation

(b). Moderation effect of gender on perceived trust

In model 3, we test for H2, where with increasing trust, males are more likely to report a higher intent of purchase. In model 3 the interaction term is supported (β = 0.197, p <  0.01), however, it is not supported in model 4 (β = 0.072, p >  0.10).

Discussion and conclusion

Online retailing operations are increasingly challenging in recent years (Fernie & Sparks, 2014). With increasing fragmentation, higher competition, and spiraling customer acquisition costs, developing ongoing purchase intentions is essential to online retailers' survival. Due to this steady increase of online retailing sales and its substantial growth, a great interest in understanding what impacts consumer’s intention to participate in online shopping is stimulated. Among the multitude of potential determinants of online purchase intention, trust in and reputation of the online retailer have been examined from different perspectives. However, gender effects remain poorly understood in the online retailing setting (Lin, Featherman, Brooks, & Hajli, 2019). This paper, aimed to shed more light on this gap and find support for gender differences in the online retailing realm. Eventually, this study found that gender differences have a significant impact on the relationship between trust in online retailers, reputation of online retailers and consumers' online purchase intention.

Accordingly, we proposed and found support for differences in how males and females rely on trust and reputation differently in developing purchase intentions. In our sample, we found that females are likely to report higher levels of purchase intentions with increasing levels of reputation (H1), whereas males are likely to report higher purchase intentions with higher levels of perceived trust (H2).

The purpose of this study was to examine whether sex-related differences drive dissimilarities in purchase intentions. This research deepens our understanding of the moderating influence of gender on the relationship between trust/reputation and purchase intention and provides insight into possible motivations for gender differences in the factors that may influence purchase intention.

These gender differences could help identify potential mechanisms in explaining differences in preferences towards future purchases. While past studies have generally focused on the differences in how trust and reputation are perceived by males and females, this association is not assessed in the context of future purchase intentions. As a result, drawing on a large survey data we aimed to assess the signalling value of trust and perceived reputation as the key drivers of purchase intentions.

The findings provide preliminary evidence to managers on the relative value of trust and reputation in eliciting repeat purchase between male and female customers. The results show that promotional and relationship building methods that highlight reputation and investments in signals highlighting reputation are critical when reaching female customers and developing relationships with them. Whereas with male customers focusing more on developing trust could result in higher future purchases.

Furthermore, this study suggest that online retailers should work on enhancing the perception of reputation when targeting female consumers; since, compared to men, females perceive more severe consequences to lack of reputation in the online retailer. Moreover, as we predicted, male did show higher purchase intention when they perceived increased in the online retailer and this may help online retailers when targeting their male consumers.

The findings of the study are subject to certain limitations. Firstly, students in an educational setting were used as representatives for online consumers although using student samples in online behavior research is common (Ahuja, Gupta, & Raman, 2003; Gao & Koufaris, 2006; Hasan, 2010; McElroy, Hendrickson, Townsend, & DeMarie, 2007) and it was found that the patterns of such behaviors are similar among non-students and students.

Secondly, while we draw on self-reported survey data, we are unable to unpack the relationship between online purchase intentions and micro-dynamics of decision making by online customers. Even though we found significant differences between males and females there may be additional heterogeneity among males or females in how they leverage reputation and trust. We call for future research to assess these differences. Thirdly, our data is a cross-sectional data from customers in Sweden. As such our findings may not be generalizable to alternate settings.

Furthermore, additional research can examine the moderating effect of other demographic and personal characteristics such as age, education, income and life style (Porter & Donthu, 2006; Wu, 2003) on the relationship with trust, reputation and online shopping intention to help businesses develop more accurate profiles of their target consumer group.

Moreover, we infer association and not causation. Causation is not implied in this study. Additional research could focus on developing experiments to evaluate causal inferences. For example, A/B testing or other approaches could help improve our understanding of the proposed relationships. Experimental studies could further help improve internal validity of our inferences. Future research could also focus on the conversion rate from purchase intentions to actual purchases. Objective purchase data could further validate our findings. Finally, we examined the linear association among the constructs, and there may be decreasing returns from investments in reputation and trust. Furthermore, it may be of interest to understand how online retailers optimize the tradeoff or complementarity between trust and reputation.

Acknowledgement

The authors thank Dr. Klas Hjort for his contribution in previous version of this study. The initial version of this study has been presented at the Nordic Retail and Wholesale Conference in Reykjavik, Iceland (2018).