Understanding the intention to use mobile shopping applications and its influence on price sensitivity
TL;DR: In this article, an online questionnaire was circulated nationwide through email to verified e-commerce users and a sample of 675 respondents was taken for analysis through structural equation modeling approach, which revealed that personal innovativeness and perceived risk play a major role in deciding the intention to use mobile shopping applications.
About: This article is published in Journal of Retailing and Consumer Services.The article was published on 2017-07-01. It has received 243 citations till now. The article focuses on the topics: Technology acceptance model & Mobile commerce.
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TL;DR: In this paper, the intention of consumers to use chatbots on smartphones for shopping was evaluated using the technology acceptance model and diffusion of innovations theory, and the intention was directly influenced only by trust, personal innovativeness, and attitude.
179 citations
TL;DR: In this article, a theoretical model is developed to examine multi-faceted risk and trust effects on consumer adoption intention, and empirical results demonstrate several trust and risk perceptions as having varying effects on consumers' m-shopping intention.
148 citations
Cites background or methods or result from "Understanding the intention to use ..."
...Although this is counter to some previous findings (e.g. Chang et al., 2016; Chen and Chang, 2011; Hanson, 2010; Hubert et al., 2017; Lian and Yen, 2014; Liébana-Cabanillas et al., 2014; Natarajan et al., 2017; Slade et al., 2015b; Yang et al., 2012; Zhang et al., 2012), it is in conjunction with others (e....
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...…findings (e.g. Chang et al., 2016; Chen and Chang, 2011; Hanson, 2010; Hubert et al., 2017; Lian and Yen, 2014; Liébana-Cabanillas et al., 2014; Natarajan et al., 2017; Slade et al., 2015b; Yang et al., 2012; Zhang et al., 2012), it is in conjunction with others (e.g. Rouibah et al., 2016; Tan…...
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...Although some research has developed insight into the role of risk and anxiety (e.g. Luarn and Lin, 2005; Natarajan et al., 2017; Wei et al., 2009; Yang, 2012), there is lack of understanding into the effects of risks towards m-shopping adoption intention, specifically, and there are repeated calls…...
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TL;DR: In this paper, the authors used an empirical model to measure merchant's intention to use a mobile wallet technology, including the variables, perceived compatibility, perceived usefulness, awareness, perceived cost, perceived customer value addition and perceived trust, and tested the mediating effect of perceived trust on the influence of perceived usefulness to predict merchants' intention.
147 citations
Cites background from "Understanding the intention to use ..."
...In online technology system, usefulness is defined as use of a technology that is useful to the user to perform a certain task (Madan and Yadav, 2016; Natarajan et al., 2017)....
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TL;DR: In this paper, the authors examined the antecedents of consumers' intention to shop at AI-Powered Automated Retail Stores and found that the Innovativeness and Optimism of consumers affect the perceived ease and perceived usefulness.
140 citations
TL;DR: In this paper, the authors investigated the acceptance of autonomous delivery vehicles (ADVs) in last-mile delivery by using an extended Unified Theory of Acceptance and Use of Technology (UTAUT2).
Abstract: The inevitable need to develop new delivery practices in last-mile logistics arises from the enormously growing business to consumer (B2C) e-commerce and the associated challenges for logistics service providers. Autonomous delivery vehicles (ADVs) are believed to have the potential to revolutionise last-mile delivery in a way that is more sustainable and customer focused. However, if not widely accepted, the introduction of ADVs as a delivery option can be a substantial waste of resources. At present, the research on consumers’ receptivity of innovations in last-mile delivery, such as ADVs, is limited. This study is the first that investigates the users’ acceptance of ADVs in Germany by utilising an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and adapted it to the context of ADVs in last-mile delivery. Quantitative data was collected through an online survey approach (n = 501) and structural equation modelling was undertaken. The results indicate that price sensitivity is the strongest predictor of behavioural intention (i.e., user acceptance), followed by performance expectancy, hedonic motivation, perceived risk, social influence and facilitating conditions, whereas no effect could be found for effort expectancy. These findings have important theoretical and practical contributions in the areas of technology acceptance and last-mile delivery.
125 citations
References
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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
56,555 citations
Book•
27 May 1998TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
42,102 citations
01 Jan 1989
TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.
40,975 citations
TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
40,720 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations