Browsing by Author "Aloysius, John A."

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    Being at the cutting edge of online shopping: Role of recommendations and discounts on privacy perceptions
    Venkatesh, Viswanath; Hoehle, Hartmut; Aloysius, John A.; Nikkhah, Hamid Reza (Pergamon-Elsevier, 2021-08-01)
    Despite the explosion of selling online, customers continue to have privacy concerns about online purchases. To alleviate such concerns, shopping sites seek to employ interventions to encourage users to buy more online. Two common interventions used to promote online sales are: (1) recommendations that help customers choose the right product either based on historic purchase correlations or recommendations suggested by the retailer; and (2) discounts that increase the value of products. Building on privacy calculus, we theorize about how and why key, representative combinations of recommendations and discounts influence the effects of inhibitors and enablers on online purchase intention. Our research design incorporated recommendations coming from different sources for the recommendation (retailer and other customers’ preferences) product relatedness (related products with historic purchases correlated to the focal product and unrelated products with no historic purchase correlation to the focal product) and two types of discounts (regular and bundle). Participants completed a browsing task in a controlled online shopping environment and completed a survey (n = 496). We found mixed results of moderating effects of recommendations and product relatedness on the effect of inhibitors and enablers on purchase intention. Although recommendations did not enhance the effects of inhibitors, they did enhance the effects of enablers on online purchase intention. We also found that product relatedness did not enhance the effect of privacy enablers on online purchase intentions. The results also showed that discounts enhance the effects of enablers, and that discounts can counteract the moderating effect of recommendations on the relationship between inhibitors and purchase intention under certain circumstances. We discuss theoretical and practical implications.
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    Customers' tolerance for validation in omnichannel retail stores: Enabling logistics and supply chain analytics
    Hoehle, Hartmut; Aloysius, John A.; Chan, Frank; Venkatesh, Viswanath (Emerald, 2018-01-01)
    Purpose: Mobile technologies are increasingly used as a data source to enable big data analytics that enable inventory control and logistics planning for omnichannel businesses. The purpose of this paper is to focus on the use of mobile technologies to facilitate customers’ shopping in physical retail stores and associated implementation challenges. Design/methodology/approach: First, the authors introduce three emerging mobile shopping checkout processes in the retail store. Second, the authors suggest that new validation procedures (i.e. exit inspections) necessary for implementation of mobile-technology-enabled checkout processes may disrupt traditional retail service processes. The authors propose a construct labeled “tolerance for validation” defined as customer reactions to checkout procedures. The authors define and discuss five dimensions – tolerance for: unfair process; changes in validation process; inconvenience; mistrust; and privacy intrusion. The authors develop a measurement scale for the proposed construct and conduct a study among 239 customers. Findings: The results show that customers have higher tolerance for validation under scenarios in which mobile technologies are used in the checkout processes, as compared to the traditional self-service scenario in which no mobile technology is used. In particular, the customers do not show a clear preference for specific mobile shopping scenarios. Originality/value: These findings contribute to our understanding of a challenge that omnichannel businesses may face as they leverage data from digital technologies to enhance collaborative planning, forecasting, and replenishment processes. The proposed construct and measurement scales can be used in future work on omnichannel retailing.
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    Exploiting big data for customer and retailer benefits: A study of emerging mobile checkout scenarios
    Aloysius, John A.; Hoehle, Hartmut; Venkatesh, Viswanath (Emerald, 2016-01-01)
    Purpose – Mobile checkout in the retail store has the promise to be a rich source of big data. It is also a means to increase the rate at which big data flows into an organization as well as the potential to integrate product recommendations and promotions in real time. However, despite efforts by retailers to implement this retail innovation, adoption by customers has been slow. The paper aims to discuss these issues. Design/methodology/approach – Based on interviews and focus groups with leading retailers, technology providers, and service providers, the authors identified several emerging in-store mobile scenarios; and based on customer focus groups, the authors identified potential drivers and inhibitors of use. Findings – A first departure from the traditional customer checkout process flow is that a mobile checkout involves two processes: scanning and payment, and that checkout scenarios with respect to each of these processes varied across two dimensions: first, location – whether they were fixed by location or mobile; and second, autonomy – whether they were assisted by store employees or unassisted. The authors found no evidence that individuals found mobile scanning to be either enjoyable or to have utilitarian benefit. The authors also did not find greater privacy concerns with mobile payments scenarios. The authors did, however, in the post hoc analysis find that mobile unassisted scanning was preferred to mobile assisted scanning. The authors also found that mobile unassisted scanning with fixed unassisted checkout was a preferred service mode, while there was evidence that mobile assisted scanning with mobile assisted payment was the least preferred checkout mode. Finally, the authors found that individual differences including computer self-efficacy, personal innovativeness, and technology anxiety were strong predictors of adoption of mobile scanning and payment scenarios. Originality/value – The work helps the authors understand the emerging mobile checkout scenarios in the retail environment and customer reactions to these scenarios.