What Matters the Most? The Key Factors That Lead to a New Service Adoption

Yang, Jun; Park, Jungkun; Endo, Seiji

doi:10.1002/hfm.20407

Cited by 2 publications

(2 citation statements)

References 70 publications

(93 reference statements)

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“…To build trust in an e-commerce platform, it is important for the sellers to display good intentions and act in a way that makes consumers feel secure. Trust has been found to play an important role in the successful adoption of online shopping (Yang et al, 2016) ,…”

Section: Trustmentioning

confidence: 99%

Modeling the Nexus Between Perceived Value, Risk, Negative Marketing, and Consumer Trust with Consumers' Social Cross-Platform Buying Behaviour in India Using Smart-PLS

Singh,

Raghuwanshi,

Sharma

et al. 2023

J. of Law and Sust. Develop.

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Purpose: The purpose of the study is to highlight the need for research on the relationship between consumer value, risk, and trust in the context of social cross-platform perceptions, specifically in India. The study aims to provide insights into how these factors influence consumers' cross-platform buying behavior, ultimately contributing to a better understanding of online shopping dynamics and informing strategies for building trust and enhancing consumer experiences in the online shopping environment. Methodology: The research methodology involved conducting a survey among 320 online shoppers in India. The objective was to investigate the relationship between social cross-platform perceptions of consumer value, risk, and trust in the context of online shopping. The researchers developed a questionnaire to collect data on perceived value, risk propensity, trust, and cross-platform buying behavior. Partial least squares (PLS) path modeling were used to analyze the data, revealing significant effects of perceived value, risk propensity, and trust on consumers' cross-platform buying behavior. Finding: This research focuses on the rise of social cross-platform buying behaviour in India and examines the influence of perceived risk, perceived value, trust, and marked negative reporting on consumer behaviour. The study found that consumers' perceptions of perceived risk and perceived value significantly impact their likelihood of using social e-commerce platforms like Meesho. Perceived risk refers to the level of uncertainty or potential harm associated with a purchase, while perceived value refers to the benefits or worth a consumer perceives from a product or service. Marked negative reporting refers to negative reviews or news articles that can impact a consumer's trust in a platform. Managerial implication: The study's managerial implications highlight the importance of reducing perceived risk, increasing perceived value, and building trust to improve social e-commerce marketing strategies. Organizations should be cautious of negative reporting and its impact on consumer behavior. Researchers can gain insights into consumer behavior and the significance of trust and perceived risk in driving purchase decisions in the context of social e-commerce marketing. To encourage cross-platform purchases, companies should address negative reports from other platforms, establish trust with consumers, and find ways to minimize perceived risk associated with social media purchases. Value: A significant contribution to the body of knowledge is achieved through testing and subsequent confirmation of the effects of online purchasing in the study model. This research enhances our understanding of the factors influencing online purchasing in the context of social cross-platform interactions. By investigating the relationship between Risk, Trust and various constructs within the social cross-platform framework, this study provides valuable insights into the dynamics of consumer behavior in the digital era.

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Section: Trustmentioning

confidence: 99%

Modeling the Nexus Between Perceived Value, Risk, Negative Marketing, and Consumer Trust with Consumers' Social Cross-Platform Buying Behaviour in India Using Smart-PLS

Singh,

Raghuwanshi,

Sharma

et al. 2023

J. of Law and Sust. Develop.

View full text Add to dashboard Cite

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“…It is an important measure in each domain in which new technologies are introduced such as in manufacturing and service industries such as in online games (Altin Gumussoy, 2016), web-based learning systems (Calisir et al, 2014), domestic cleaning robots (N.-H. Chen & Huang, 2016), knowledge map systems (K.-W. Lee et al, 2017), and new service technologies (W.-I. Lee et al, 2011;Yang et al, 2016). Since technology acceptance has been investigated in various application domains there is no common agreement on the elements of the psychological construct.…”

mentioning

confidence: 99%

The technology acceptance scale: Its Bayesian psychometrics assessed in a factor analysis via Markov chain Monte Carlo models

Schmid¹

2021

Hum Ftrs & Erg Mfg Svc

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The technology acceptance scale (TAS) by van der Laan, Heino, and De Waard (1997) measures the psychological construct of the same term as a sum of attitudes of an operator toward a specific complex sociotechnical system. The TAS has been claimed to comprise two subscales, usefulness and satisfaction. However, recent empirical work has found evidence for only one underlying factor. To provide further insight into the factor structure of the TAS, this study adopts a Bayesian exploratory factor analysis (BEFA) to analyse the data of a flight simulation study regarding single pilot operations. A series of Markov chain Monte Carlo (MCMC) models is used to assess the latent factor structure of the TAS for the two different crewing conditions and their corresponding workstation and cockpit setups of the copilot. A reliable step-by-step data analysis of the MCMC models provides evidence for a one-factor solution of the scale. The divergence to the previous studies which claim two factors can be due to the different applications as well as due to different statistical paradigms and methodological issues in exploratory factor analysis.

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What Matters the Most? The Key Factors That Lead to a New Service Adoption

Cited by 2 publications

References 70 publications

Modeling the Nexus Between Perceived Value, Risk, Negative Marketing, and Consumer Trust with Consumers' Social Cross-Platform Buying Behaviour in India Using Smart-PLS

Modeling the Nexus Between Perceived Value, Risk, Negative Marketing, and Consumer Trust with Consumers' Social Cross-Platform Buying Behaviour in India Using Smart-PLS

The technology acceptance scale: Its Bayesian psychometrics assessed in a factor analysis via Markov chain Monte Carlo models

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