Customer loyalty is tested every day in the retail sector and the past years have been challenging for retailers to keep their existing, loyal customers. Covid-19 outbreak caused significant changes in consumers’ purchasing behavior because during the pandemic, consumers were forced to adapt new patterns of shopping which is likely to reflect also in customer loyalty. To better understand the temporal development of customer loyalty, we investigate how cognitive, affective, and social drivers of loyalty differ between 10,044 Trustpilot reviews before, during and after the Covid-19 pandemic. We investigate customer loyalty in the retail sector using an objective and a novel approach; thus we based this study on user-generated content in the form of online reviews in contrast to the traditional survey-based measures of customer loyalty.

Sustainability rears its head in the current online marketing and virtual store -research. Sustainability considerations involve pro-environmental-, social- and economic values as well as future generations and continuous innovation (Hanss and Böhm, 2012). Central in the sustainability research is sustainable consumer behavior, which has been found to be subject of intensions varying across different types of consumers, issues, and product categories (O’Rourke and Ringer, 2016). Determining consumers’ general egoistic, altruistic and biospheric values (e.g., De Groot and Steg 2008; Steg et al., 2014) have resulted quite complex and not always so generalizable structural models for sustainable behavior. While value -research has been dominant in determining the sustainability intensions and eventual behavior, there are relatively little solid theories and understating about different psychological processes behind sustainable behavior. Furthermore, the consideration of multiple sustainable consumer behavior outcomes seems to be limited, which can also hamper the development of models and theories (see e.g., Hulland and Houston 2021).

B2B marketers increasingly encounter a pressure to be digitally present in digital channels and to generate content that is tempting in driving potential customers to interact with the company online (Wiersema, 2013; Andersson & Wikström 2017). This is what B2B lead nurturing is about as the objective of lead nurturing is to provide the audience with relevant and valuable content which leads to an increased brand interest and awareness, with the goal of bringing in new customers (Marketo, 2023). To better understand how prospects and leads react to digital content, companies can build lead scoring into a strategic tool for the salespeople to qualify the prospective customers down to a list of leads, meaning prospects who are considered the most likely to convert to a positive business outcome and to be contacted in person by the salespeople (Järvinen & Taiminen 2016; Paschen et al. 2020).

Virtual reality (VR) has been set with an expectation of becoming the technology to enable new electronic businesses on metaverse platforms. VR is an application of three-dimensional computer graphics to create a convincing virtual environment where users can interact (Cowan & Ketron, 2019). The most used immersive VR devices are head-mounted displays. Importantly, in addition to visual sense, auditory, haptic, and olfactory senses stimulating devices are used in conjunction with head-mounted displays to reach a multi-sensory VR experience (Xi & Hamari, 2021). VR has been part of the brand strategy for various marketing endeavours (Cowan & Ketron, 2019) such as viewing furniture or kitchen setups by IKEA or creating virtual customer experiences in the real estate sector. However, VR’s impact on the consumer thought model has not been thoroughly examined. This extended abstract contributes to this shortcoming.

The study applies the concept of consumer innovativeness to predict Indian consumers’ purchase intention of electric car and wood fibre-based clothing. Consumers with high novelty seeking attitudes appear to be early adopters of these two eco-innovations suggesting that marketers need to highlight the novelty attributes of these sustainable products.

Researchers have yet to investigate whether it is beneficial for exporters to engage in greater levels of product adaptation in their export operations, or whether there is some limit to the amount of adaptation exporters should engage in. We posit that customer value creation, a central marketing concept and a mechanism to achieving market and financial goals in business to business markets, is a core outcome of export product adaptation activities. In order to explore the routes by which adaptation may shape export customer value creation, we adopt a multi-faceted conceptualization of firm-level product adaptation, comprising export product adaptation (i) quantity, (ii) intensity and (iii) novelty. Drawing on survey data from 249 Finnish exporters involved in business-to-business activities, we find evidence to support the claim that the impact of export product adaptation on export customer value creation is contingent on various factors, and we identify instances where greater adaptation is beneficial for export customer value creation, and instances where greater export product adaptation is potentially harmful for export customer value creation.

This research was conducted in order to examine the effects of user socio-demographics and recently introduced streamlined technology readiness index TRI 2.0 (Parasuraman & Colby, 2015) on mobile device use in B2B digital services. Mobile adoption has been studied from a consumer perspective, but to the best of the authors’ knowledge, very few studies explore mobile use in B2B markets. Mobile marketing is becoming a strategic effort in companies, as digital services not only in B2C but also in B2B sector are getting increasingly mobile (Leeflang, Verhoef, Dahlström & Freundt 2014). This raises an interest to better understand the characteristics of those mobile enthusiasts who primarily use B2B services via a mobile device rather than via a personal computer. The study tests hypotheses with a large data set of 2,306 business customers of which around 10 percent represent these innovative mobile enthusiasts. Technology readiness is an individual’s propensity to embrace and use new technologies for accomplishing goals in home life and at work (Parasuraman & Colby, 2015; Parasuraman, 2000). Parasuraman and Colby (2015) recently introduced an updated version of the original Technology Readiness Index (TRI 1.0) scale called TRI 2.0 to better match with the recent changes in the technology environment. At the same time they streamlined the scale to a compact 16-item version so that it is easier for researchers to adopt it as a part of research questionnaires. Likewise the original scale, TRI 2.0 consists of four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism and innovativeness are motivators of technology adoption while discomfort and insecurity are inhibitors of technology readiness, and these motivator and inhibitor feelings can exist simultaneously (Parasuraman & Colby, 2015). Optimism is a general positive view of technology containing a belief that technology offers individuals with increased control, flexibility and efficiency in their lives. Innovativeness refers to a tendency to be a pioneer and thought leader in adopting new technologies. Discomfort reflects a perception of being overwhelmed by technology and lacking control over it. Moreover, insecurity reflects distrust and general skepticism towards technology, and includes concerns about the potential harmful consequences of it. As individuals differ in their propensity to adopt new technologies (Rogers, 1995), the authors propose that technology readiness influences mobile device use of B2B customers: H1: Optimism has a positive effect on mobile device use of B2B digital services. H2: Innovativeness has a positive effect on mobile device use of B2B digital services. H3: Insecurity has a negative effect on mobile device use of B2B digital services. H4: Discomfort has a negative effect on mobile device use of B2B digital services. The earlier literature argues that socio-demographic factors such as gender (Venkatesh & Morris, 2000; Chong, Chan & Ooi, 2012), age (Venkatesh, Thong & Xu, 2012; Chong et al., 2012; Kongaut & Bohlin 2016), education (Agarwal & Prasad, 1999; Chong et al., 2012; Puspitasari & Ishii 2016) and occupation (Okazaki, 2006) influence technology adoption behavior in general, and mobile adoption in particular. For example, men are nearly twice as likely as women to adopt mobile banking, and age is a negative determinant (Laukkanen, 2016). Higher educated use mobile devices more for utilitarian purposes, while lower educated use mobile devices more for entertainment (Chong et al., 2012). Moreover, research suggests that occupational factors influence mobile use (Okazaki, 2006). The authors hypothesize: H5: Males are more likely than females to use mobile device for B2B digital services. H6: Age has a negative effect on the use mobile device for B2B digital services. H7: Customers with higher education level have a higher likelihood for using mobile device for B2B digital services than customers with lower education level. H8: Occupation has an effect on the use mobile device for B2B digital services. The study tests hypotheses with a data collected among B2B customers of four large Finnish companies, all representing different industry fields. The large sample (n=2306) consists of procurement decision-makers all experienced with using B2B digital services. The sample shows that over 90 percent of the B2B customers are still using a computer (laptop or desktop computer) as their primary access device for digital services in their work. The sample divides between females and males in proportion to 46 and 54 percent respectively. University degree represents a majority with 42 percent, while only 2,7 percent of the respondents have a comprehensive or elementary school education. Over half of the sample represent top management or middle management with 24,6 and 28,4 percent respectively, while 9 percent are entrepreneurs, 21,2 percent represent experts, and 16,7 percent are officials or employess. Mean age of the respondents is 51,6 years, ranging from 18 to 81 years. The study uses logistic regression analysis with backward stepwise method in which the dependent variable is a dichotomous binary variable indicating the respondent’s primary access device for B2B digital services with 0=computer and 1=mobile device. As for the independent variables, the study measures individual’s technology propensity with recently introduced 16-item TRI 2.0 scale from Parasuraman and Colby (2015) using a five-point Likert scale ranging from Strongly disagree=1 to Strongly agree=5. The authors used confirmatory factor analysis to verify the theory-driven factor structure of the TRI 2.0 scale, i.e. optimism, innovativeness, discomfort, and insecurity. The analysis show that the measurement model for the TRI 2.0 scale provides an adequate fit and standardized regression estimates for all measure items exceed 0.60 (p<0.001) except for one item in discomfort (β=0.516) and one item in insecurity (β=0.480). After removing these two items the model shows an excellent fit with χ2=478.033 (df=71; p<0.001), CFI=0.965, RMSEA=0.050. Moreover, discriminant validity is supported, as the square root of the average variance extracted (AVE) value of each construct is greater than the correlations between the constructs (Fornell & Larcker, 1981). In addition, composite reliability values vary from 0.726 to 0.852 supporting convergent validity of the TRI 2.0 factors (Table 1). Thereafter, the factor scores of the latent factors showing sufficient internal consistency were imputed to create composite measures. These composite measures were used as independent variables in the logistic regression model. With regards to socio-demographic variables, age is measured as a continuous variable, while gender, education, and occupation are categorical independent variables in the model. The results of the logistic regression analysis show that innovativeness, insecurity, age, and occupation are statistically significant predictors of mobile device use in B2B services, supporting hypotheses H2, H3, H6, H8. The stepwise analysis procedure removed optimism (p=0.860), education (p=0.789), gender (p=0.339), and discomfort (p=0.159) from the model as they proved to be non-significant predictors of mobile device use. The results indicate that occupation is the strongest predictor for mobile device use in B2B digital services so that the top management has the greatest likelihood as the odds ratios of middle management, experts, and officials/employees are 0.610, 0.282, and 0.178 respectively. This means that, for example, the odds of the top management using mobile device as their primary channel for B2B digital services are 1.64 (1/0.610) times greater than the odds of the middle management, and 5.62 (1/0.178) times greater than the odds of the officials/employees. Interestingly the β-value for the entrepreneurs is positive indicating that their likelihood for mobile device use is even greater than the likelihood of the top management. However, the p-value (0.913) indicates that the difference is not statistically significant. With regards to age of the B2B customer, the results indicate a negative relationship with mobile device use. The odds ratio [Exp(β)=0.979] claims that the odds of a B2B customer to use mobile device as the primary channel for digital services decrease by 2 percent for each additional year of age. Regarding the TRI 2.0 constructs, the results show that innovativeness is a highly significant positive predictor for mobile device use, while perceived insecurity has a negative effect (Table 2). Literature suggests that B2B customers increasingly use mobile devices but yet little is known about those individuals most enthusiastic in using B2B digital services via a mobile device. Thus, the current study attempts to better understand those mobile enthusiasts who among the first have adopted mobile devices as their primary method to access B2B digital services. The results suggest that occupation is the most significant predictor of mobile use among B2B customers, implying that top managers are among the most likely to adopt and use mobile device for business services. Moreover, younger B2B customers use mobile devices more eagerly as the results suggest the likelihood for mobile device use degreases by 2 percent with every added year of age. The results further imply that out of the four TRI 2.0 dimensions innovativeness and insecurity influence in the mobile device use of B2B customers, innovativeness positively and insecurity negatively as the theory proposes. Innovativeness represents individual’s tendency to be a pioneer and thought leader in terms of technology adoption, while insecurity stems from the general skepticism and distrust of technology. These results imply that B2B customers who mainly access B2B digital services via a mobile device are open minded towards the possibilities new technologies can provide for them. Moreover, it appears that those B2B customers still accessing digital services primarily via a computer are more skeptical than mobile users towards technology in general. Compared to the use of mobile devices for individual purposes, business related use is more functional in nature, and thus, mobile devices and technologies must be convenient to use, offer real benefits for example in forms of mobility and portability, and be reliable in order for B2B customers to use them. Interestingly, our results do not support the effects of generally positive attitudes towards technology reflecting optimism, or discomfort of using technologies to influence mobile use among B2B customers. In addition, there are organizational factors (e.g. voluntariness of use) that the authors omit in the current study. These may limit the findings. Mobility will be a key driver in the ongoing digital revolution of marketing and sales. Understanding online behavior of mobile enthusiasts assists B2B marketing and sales leaders to plan and implement more effective mobile marketing strategies. Rogers (1995) has shown that the majority will follow the early adopters, and the adaptation cycle has even shortened during the last years (Downes & Nunes, 2014). Thus, mobile devices are evidently becoming the primary method in accessing B2B digital services.

The objective of this study is to test how five theory-driven adoption barriers and three key consumer demographics influence consumer adoption versus rejection decisions in two seemingly similar service innovations. The earlier literature on innovation diffusion recognizes two streams of research: one focusing on innovation adoption and acceptance of innovations, and the other stream, though less traveled, calling attention to innovation resistance. All innovations face a certain degree of resistance among consumers depending on consumer characteristics and the innovation itself. The literature argues that consumers can simultaneously express views that are both favorable and unfavorable towards the innovations (Ferreira, da Rocha, & da Silva, 2014) and thus both resistance and adoption can coexist during the lifetime of an innovation (Ram, 1987). Thus it is reasonable to explore how innovation resistance influences consumer decisions in different service innovations. Initially scholars explained resistance to innovations through two constructs, habit or satisfaction with an existing behavior and perceived risks associated with innovation adoption (Sheth, 1981). Ram and Sheth (1989) provide a more comprehensive view to the phenomenon by explaining consumer resistance through functional and psychological barriers that they further divide into five distinct barriers, namely usage, value, risk, tradition and image. This study tests how these five adoption barriers as well as three consumer demographics, gender, age, and income, influence consumer adoption versus rejection decisions in Internet and mobile banking. An effective total sample size of 1,736 consumer responses were collected from Finland. Logistic regression analysis finds that the value barrier is the strongest inhibitor of Internet and mobile banking adoption. In addition, while the image barrier slows down mobile banking adoption, the tradition barrier explains the rejection of Internet banking. In addition, age greatly explains this behavior and the results show that younger segments have a significantly greater likelihood of Internet banking adoption than their older counterparts. Contrary to Internet banking, it appears that gender significantly contributes to mobile banking adoption and the intention to use it. The results predict that males have nearly two times greater likelihood towards adoption compared to females.