Strategic Use of Bundling for Reducing Consumers’ Perceived Risk Associated with the Purchase of New High-Tech Products

Sarin, Shikhar; Sego, Trina; Chanvarasuth, Nataporn

doi:10.1080/10696679.2003.11658502

Cited by 103 publications

(64 citation statements)

References 45 publications

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“…Also, by bundling lesser known and trusted products together with ones that are already part of consumers' usage patterns, users' perceptions of risk associated with the new product decreases. For example, Sarin and Sego (2003) show, that bundling high-tech products decreases consumers' perception of risk, which in turn results in increased acceptance of the products. In the case of MaaS, even though shared services are gaining wider acceptance, their use could be accelerated by bundling them together with more popular modes such as public transport and taxi.…”

Section: Bundlingmentioning

confidence: 99%

The potential of mobility as a service bundles as a mobility management tool

2018

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The Mobility as a Service (MaaS) concept has recently taken the transport industry by storm. However, as applications and research on it are limited, there is still little evidence on what this new phenomenon could bring to the transport sector. This paper aims to provide initial insights into whether MaaS product bundles (monthly subscription plans) can be used as a mobility management tool to promote shared modes. Data from an original survey, specifically designed to study MaaS plans, was used with Greater London as the case study area. The results show, that even though respondents do not prefer shared modes in their MaaS plans, a significant number of them are willing to subscribe to plans that include these modes. Once they have subscribed, over 60% of them indicated that they would be willing to try transportation modes that they previously did not use if their MaaS plans included them. These initial results show evidence that MaaS bundles can indeed be used as a mobility management tool to introduce more travelers to shared modes.

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Section: Bundlingmentioning

confidence: 99%

The potential of mobility as a service bundles as a mobility management tool

2018

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“…Electronic banking is a technology-enabled channel and consumers' perceive the use of electronic banking as a risky decision because technology-enabled services exhibit pervasive technological, unfamiliar and ambiguous stimuli (Davidow, 1986). Therefore, when consumers decide to use electronic banking, they are exposed to uncertainties such as the availability, the compatibility, and the performance of the complementary electronic banking channels (Sarin, Sego and Chanvarasuth, 2003). Ho and Ng (1994) and Lockett and Littler (1997) empirically support that the use of electronic banking involves risk.…”

Section: Perceived Risk Factorsmentioning

confidence: 99%

A logit analysis of electronic banking in New Zealand

Gan

Clemes

Limsombunchai

et al. 2006

International Journal of Bank Marketing

140

114

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Financial institutions have been adopting internet banking since the mid 90s, predominantly due to lower operating costs associated with internet banking, and pressure from non-banks interested in entering the internet banking market. In addition, customers utilizing internet banking facilities are increasing, as the cost savings on transactions over the internet are substantial (Mols, 1998;Sathye, 1999). Internet banking enables speedy transactions, access, time and money savings through providing free paper, and complete and up-to-date transactions. The competitive landscape of financial institutions is shifting as internet banking is no longer a competitive advantage but a competitive necessity for banksThe literature has featured numerous published research papers, articles and books addressing a wide range of issues relating to electronic banking (see Pyun, Scruggs and Nam, 2002;Li, 2002;Mols, 1999). However, there is little empirical research on the effect of electronic channels on consumer's buying behaviour (Hendrikse and Christiaanse, 2000) or banking channel preferences in New ZealandThe purpose of this research is to examine consumers' decision-making between electronic banking and non-electronic banking in New Zealand. The research uses the consumer decision making process (or paradigm) to identify factors that consumers use when deciding between electronic banking and non-electronic banking. These factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics, and individual factors. The demographic variables include age, gender, marital status, ethnic background, educational qualification, employment, income, and area of residence.

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“…For example, service quality dimensions such as reliability, assurance and responsiveness are positively related to the use of electronic banking [16] . Furthermore, consumers' decision to use electronic banking is negatively related to financial, performance, physical risk, social and psychological risks [17] .…”

Section: Introductionmentioning

confidence: 99%

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

Gan¹,

Limsombunc²,

Clemes³

et al. 2005

J. of Social Sciences

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Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN) to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN), a special class of neural networks and a MLFN with a logistic model on consumers' choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers' use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN) exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

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Strategic Use of Bundling for Reducing Consumers’ Perceived Risk Associated with the Purchase of New High-Tech Products

Cited by 103 publications

References 45 publications

The potential of mobility as a service bundles as a mobility management tool

The potential of mobility as a service bundles as a mobility management tool

A logit analysis of electronic banking in New Zealand

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

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