The effect of product review balance and volume on online Shoppers' risk perception and purchase intention

Yang, Jing; Sarathy, Rathindra; Lee, JinKyu

doi:10.1016/j.dss.2016.06.009

Cited by 100 publications

(73 citation statements)

References 40 publications

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“…To resolve this, studies have focused on segmentation based on generation [69][70][71], instead of age. For instance, [13] found that perceived risk does not influence Gen Y e-shopping behavior, and [71] showed that the capacity to take risk is higher among Gen Y than Gen X and hence these groups are to be treated differently, especially when understanding perceived risk [26]. Recent studies on e-shopping adoption have split customers based on FLC stages [36,72], due to its established significant impact on time pressure and situational changes in families which has been proven to be one of the crucial factors affecting e-shopping [52,73].…”

Section: Socio-demographics On Perceived Riskmentioning

confidence: 99%

“…Hence, e-shopping researchers have noted that to make these people shop online, it is important for retailers to focus on reducing risks rather than concentrating on benefits when setting up their online presence [12]. E-shopping researchers have emphasized that there exists a knowledge gap on the factors that act as barriers of adoption of e-shopping technology in developing countries [5,9,[23][24][25][26]. Moreover, while several studies have highlighted the importance of considering the multi-dimensionality of perceived risk [15,27], few studies have been conducted taking the various dimensions into account [6][7][8]10,28].…”

Section: Introductionmentioning

confidence: 99%

“…Given that e-shopping is an integral part of today's economy, understanding the e-shopping behavior across all age groups becomes important, as the adoption of technology by older adults plays a vital role in economic development [49]. Hence to understand the effect of different demographic variables on online behavior, a further split age-based research on technology adoption is essential [26,31,50]. This study acknowledges that there are barriers that hinder people of different ages from shopping online and explores this further.…”

Section: Introductionmentioning

confidence: 99%

“…The dearth of studies on e-shopping behavior among Indian women has also been an outcome of the nascency of e-commerce in India [51]. Women perceive higher risks in e-shopping than men [16,26,52] especially in developing countries like India [21,51]. Given that in most cultures, women assume the responsibility of household shopping and purchase decisions [53][54][55] and FLC stages have a greater effect on women than men [56], there is a need to explore the role of perceived risk in deterring Indian women of different FLC stages from participating in e-shopping activities.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Influence of Perceived Risk Dimensions on e-Shopping Behavioural Intention among Women—A Family Life Cycle Stage Perspective

Amirtha

Sivakumar

Hwang

2020

JTAER

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The relationship between perceived risk and behavioral intention (BI) in e-shopping, based on family life cycle (FLC) stages, has been analyzed in this work. Although FLC stages are considered to have a better predictive ability than age, few e-shopping studies have concentrated on understanding its effects. This study, as a pioneering effort, has divided Indian women based on nine FLC stages and has studied the role of ten dimensions of perceived risk on BI to shop online across each life cycle stage. Results show that different facets of risks had distinct effects on purchase behavior among women belonging to different FLC stages. In effect, this study shows the importance of splitting people based on FLC stages in e-marketing and its value in making marketing decisions.

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Section: Socio-demographics On Perceived Riskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Perceived Risk Dimensions on e-Shopping Behavioural Intention among Women—A Family Life Cycle Stage Perspective

Amirtha

Sivakumar

Hwang

2020

JTAER

View full text Add to dashboard Cite

show abstract

“…Von et al [11] investigated how average consumer ratings, and consumer reviews influenced online purchasing decisions invention of younger and older adults. Furthermore, many scholars found some other factors which affect the consumer's online purchase motivations or intentions, such as online social ties and product-related risks [12], media channels [13], product review balance and volume [14], etc.…”

Section: Product Purchase Decision-makingmentioning

confidence: 99%

Group Decision Algorithm for Aged Healthcare Product Purchase Under q-Rung Picture Normal Fuzzy Environment Using Heronian Mean Operator

Yang¹,

Li²,

Garg³

et al. 2020

IJCIS

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With the intensification of the aging, the health issue of the elderly is arousing public concern increasingly. Various healthcare products for the elderly are emerging from the market, thus how to select suitable aged healthcare product is critical to the wellbeing of the elderly. In the literature, nonetheless, a comprehensive and standardized evaluation framework to support healthcare product purchase decision for the aged is currently lacking. This paper proposes a novel group decision-making method to aid the decision-making of aged healthcare product purchase based on q-rung picture normal fuzzy Heronian mean (q-RPtNoFHM) operators. In it, firstly, a new fuzzy variable called the q-rung picture normal fuzzy set (q-RPtNoFS) is defined to reasonably describe different responses to healthcare product evaluation, for which, some definitions including operational laws, a score function, and an accuracy function of q-RPtNoFSs are introduced. Then, two q-RPtNoFHM operators are presented to aggregate group decision information. In addition, some properties of q-RPtNoFHM operators, such as monotonicity, commutativity, and idempotency, are discussed. Finally, an example on antihypertensive drugs purchase is gave to illustrate the practicality of the proposed method, and conduct sensitivity analysis to analyze the effectiveness and flexibility of proposed methods.

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Why do people buy virtual clothes?

Khelladi,

Lejealle,

Rezaee Vessal

et al. 2023

J of Consumer Behaviour

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Virtual clothes consumption is part of the digital virtual consumption practices of the global NFT phenomenon and the global digital fashion trend. While cutting‐edge technologies shape customer experience and behavior, research examining how consumers desire, own, and experience these technologies in general, and virtual objects in particular, remains scarce. Adopting the uses and gratifications (U&Gs) theory and building on research related to brand roles and category, the present research aims to unveil the psychological determinants of purchasing NFTs‐virtual clothes. A mixed‐method, two‐stage approach was adopted. First, a qualitative study and a pilot study aimed to capture and understand what motivates people to buy NFTs‐virtual clothes and identify contextualized U&Gs. Second, two original confirmatory studies verified the U&Gs of purchasing virtual clothes and investigated how they differ based on brand category (luxury vs. non‐luxury). The results show that enjoyment, social interactivity, and curiosity are the main predictors of NFTs‐virtual clothes purchases. Consumers mainly seek hedonic, social, and technological gratifications as part of the digital fashion consumption trend. Brand managers must recognize virtual clothing gratifications for effective virtual world positioning. Products should encourage networking, community, and user interaction. Demographics and virtual experience should be considered to understand NFT‐virtual clothes motivations.

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The effect of product review balance and volume on online Shoppers' risk perception and purchase intention

Cited by 100 publications

References 40 publications

Influence of Perceived Risk Dimensions on e-Shopping Behavioural Intention among Women—A Family Life Cycle Stage Perspective

Influence of Perceived Risk Dimensions on e-Shopping Behavioural Intention among Women—A Family Life Cycle Stage Perspective

Group Decision Algorithm for Aged Healthcare Product Purchase Under q-Rung Picture Normal Fuzzy Environment Using Heronian Mean Operator

Why do people buy virtual clothes?

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