The study focuses on the effect of four primary characteristics derived from the literature, including trust, product variety, convenience, and privacy, so as to identify how customer purchasing ...behavior reflects online shopping trends. The researcher used quantitative research approach to documents responses of respondents. Students attending Thal university & Sargodha university are mostly those engaged in business programme at respective institutions, such as the Department of Business Administration and Noon Business school was accessed to collect the data through questioner. The researchers use structural equational model to test hypothesis of current research, analyze the results and reach the desired conclusion and thus recommending some suggestions. According to the findings of statistical analysis of data, customers trust, product variety, convenience and privacy has statistically significant relationship with the online consumer behaviour. This conclusion is vital for the marketers to facilitate the usage of online shopping platforms in order to save more time and attract more people to buy items online.
Offline social interactions and online shopping each have been studied extensively. Despite the importance of each construct, little is known about the effects of offline social interactions on ...online shopping. This study examines three research questions: (1) how offline social interactions affect online shopping in general, (2) how active and passive offline social interactions exert different influences on online shopping, and (3) how online shopping preferences moderate the influences from the two types of offline social interactions. Our empirical analyses provide three substantive findings. First, overall offline social interactions have a positive impact on online shopping demand. Second, while active offline social interactions have a positive informational influence on online shopping demand, passive offline social interactions have a negative normative influence on it. Third, online shopping preferences weaken both the positive informational and negative normative influences from both of offline social interactions. We also discuss theoretical and managerial implications.
In this study, a three-step methodology is proposed. To begin with, a total of seven main criteria and 23 sub-criteria that affect the selection of online shopping websites are determined by ...searching the literature and interviewing people. Next, a questionnaire is applied to the people from Turkey and Croatia. It is evaluated using AHP methodology to find the main and sub-criteria weights from the perspective of Turkish and Croatian people. Furthermore, a second questionnaire for ranking three popular online shopping websites in Turkey has been applied. Finally, online shopping websites are ranked as B, A, and C based on the scores obtained from the second questionnaire and criteria weights found in the second step. After estimating the decision weights of the sample from Croatia, we use that as a “what if” analysis for websites A, B, and C. Customers’ shopping behaviors from those online shopping websites are analyzed using SPSS.
The COVID-19 pandemic has created a new reality for consumers all around the globe. To cope, users of digital technologies have faced the necessity of adopting and using specific technologies ...practically overnight. They are doing this under the condition of social isolation, all while facing the fear of catching the disease. The purpose of the paper is to study the way unexpected circumstances cause disruptions in existing theoretical models and their implications for the post-COVID-19 era. Therefore, the paper examines the unified theory of acceptance and use of technology (UTAUT) model under the circumstances of the COVID-19 pandemic and social isolation, and it identifies herd behavior as a possible new mechanism affecting behavioral intention under these unique decision-making circumstances. Behavioral intention toward online shopping was analyzed using data from 420 individuals aged 60 and older who present an increasingly important potential market for electronic commerce and who are particularly affected by COVID-19. The main results show that performance expectancy still has the most important influence on behavioral intention, whereas the impact of social influence was not supported under these conditions. Rather, herd behavior was identified as particularly influential for behavioral intention. Based on the study results, the option to reconsider the social influence factor in the UTAUT model and its possible complementary mechanisms are discussed.
•Online shopping adoption among older adults during the pandemic was reviewed.•Social influence does not have a major role in behavioral intention to adopt online shopping.•Contrary to our expectations, COVID-19 fear does not influence behavioral intention to adopt online shopping.•Herd behavior importantly influences behavioral intention in the pandemic situation.•Complementary mechanisms to social influence in UTAUT are discussed.
This research contributes to broadening understanding of online retailing across electronic channels (e-channels, e.g., mobile devices) and e-channel touchpoints (e.g., mobile shopping apps) from a ...consumer perspective. Based on the multichannel retailing approach and theoretical considerations, the authors suggest an enhanced perspective on the online retailing environment and validate this multichannel e-commerce perspective by conducting both an online survey (N = 502) and an experimental study (N = 126). The results indicate that online retailing can be classified into four e-commerce categories that entail individual e-channel touchpoints, emphasizing the need for a more differentiated consideration of “the online channel.” This work advances marketing research and practice by illustrating that both technology-related quality and context-related situational benefit affect consumers' utilization of e-channels. Further findings show that retailers can enhance consumers' shopping experiences by providing alternative e-channel touchpoints (i.e., specific digital shopping formats) that contribute differently to the online customer journey.
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•A framework for the multichannel e-commerce environment•Identification of four categories of e-channels (E-, M-, IETV- and C-Commerce)•Approach to capture the online customer journey across retailer-owned touchpoints•E-channel evaluation is based on perceived quality and situational benefit.•Satisfy heterogeneous needs by designing and combining e-channel touchpoints
The purpose of this article is to identify which dimensions of online convenience affect consumers’ intention of using online shopping and explore a conceptual model to measuring consumer perceptions ...of online shopping convenience in order to surpass the shortcomings of previous studies that did not examine the consequences of convenience shopping experience. A sample of 250 Portuguese young individuals participate in the empirical study. Confirmatory Factor Analysis (CFA) and a covariance-based Structural Equation Model (CB-SEM) were used to validate the measurement model and to test the relationships in the model. The results reveal that Possession, Transaction, and Evaluation are the dimensions with more influence in online shopping convenience. The outcomes of this study extend previous works on online convenience and help to understand which factors drive online satisfaction and enhance behavioral intentions and e-WOM. Contributions to the body of knowledge and the implications for e-commerce retailers are presented. In face of the findings, retailers should be conscious that customer expectations of online convenience have increased as a natural response to the service innovations introduced by website managers and marketers. Therefore, frequent monitoring of consumers’ perceptions and expectations about online convenience is a prerequisite for achieving continuous improvement in rendering highly convenient online service.
The amount of food sold online is increasing, but it accounts for a small share of total e-commerce. In this study, we investigate the factors that influence individuals' likelihood to buy food ...online. Applying a logit model to a sample of 34,488 respondents who participated in the Italian National Institute of Statistics multipurpose survey ‘Aspects of Italian Daily Life’, we explore the effects of socio-demographics and situational factors. We found that the food-online consumer is likely to be a young, well-educated, female, living in a small family, with a very good or adequate overall economic condition. Among situational factors, working time, being obese, having health problems, and practising a sport regularly positively affect the probability to buy food online. Surprisingly, distance from brick-and-mortar stores and car possession are not predictors of online shopping. These findings can support marketers and retailers in defining their marketing strategies and enhance the knowledge of this emerging food market.
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•Socio-demographic traits and situational factors influence e-grocery shopping.•Smaller households are more likely to buy food online.•Obese individuals and people with poor health are likely to buy food online.•The distance from the stores does not influence the choice to buy food online.
Evidently, the Internet has resulted in a fundamental shift in retailing practice, creating a shift in both consumer and business behavior, which has been compared to that of the Industrial ...Revolution. The purpose of this paper is to analyze customer satisfaction in e-commerce market. In particular, we determine the factors that affect customer e-satisfaction and the relationship between customer satisfaction and consumer spending in e-commerce retailing. We focus on how American based e-commerce firms are impacted by these developments and how marketing practices have reflected the developing e-commerce situation. The results show that customer satisfaction does have an impact on consumer spending in American based e-commerce retailers. Further, the relationship between customer satisfaction and consumer spending is positive, where higher e-satisfaction results in more spending in e-commerce. The results also show that there is a direct relationship among e-service quality, e- satisfaction and e-loyalty in terms of online spending by consumers. However, the analysis shows that e-commerce still faces challenges compared with traditional offline retailers since customers cannot feel and try the products, and may end up choosing the products that they do not want.
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