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Source publication
Purpose
Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective...
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Context 1
... are the integers on a 5-star scale. In Table 3, a comparison is made for each data size, wherein "% of data sparsity" is calculated as the percentage of 1 -("Number of Rating"/ "Users X Items Rating"). The rest of the attribute description can be easily can be easily comprehended from the table. ...Citations
... The benefits of CI-enabled recommendation in IM are a better e-customer experience, higher digital product discoverability, higher engagement with e-customers, increased revenue, and easier inventory management. The more insights the system has, the more accurate the recommendations will be (Behera et al., 2020a(Behera et al., , 2020b. ...
... A test instance yielded 84% accuracy of the recommendation. Behera et al. (2020) developed a rule based automated ML approach that also targets the same approach but on a different problem domain. Their recommender engine targets company performance forecasting when introducing new products or services. ...
Shopping convenience can be turned into a competitive advantage for online grocery retailers. Consequently, we study how personalized product recommendations (recommendation agents) and price promotions (algorithmic pricing) compensate for the negative impact that consumer's perceived cognitive effort causes on loyalty. By default, the relationship from perceived cognitive efforts to attitudinal and behavioral loyalty is negative, yet these results demonstrate that personalized price promotions lessen the negative impact, while personalized product recommendations do not have such an influence. The findings contribute to a better understanding of personalized marketing activities in today's data-driven online grocery retailing.
Purpose
The purpose of this study was to ascertain how real options investment perspective could be applied towards monetization of customer futures through the deployment of machine learning (ML) and artificial intelligence (AI)-based persuasive technologies.
Design/methodology/approach
The authors embarked on a theoretical treatise as advocated by scholars (Cornelissen, 2019; Barney, 2018; Cornelissen, 2017; Smithey Fulmer, 2012; Bacharach, 1989; Whetten, 1989; Weick,1989). Towards this end, theoretical argumentative logic was incrementally used to build an integrated perspective on the deployment of learning and AI-based persuasive technologies. This was carried out with strategic real options investment perspective to secure customer futures on m-commerce apps and e-commerce sites.
Findings
M-commerce apps and e-commerce sites have been deploying ML and AI-based tools (referred to as persuasive technologies), to nudge customers for increased and quicker purchase. The primary objective was to increase engagement time of customers (at an individual level), grow the number of customers (at market level) and increase firm revenue (at an organizational level). The deployment of any persuasive technology entailed increased investment (cash outflow) but was also expected to increase the level of revenue and margin (cash inflow). Given the dynamics of market and the emergent nature of persuasive technologies, ascertaining favourable cash flow was challenging. Real options strategy provided a robust theoretical perspective to time the persuasive technology-related investment in stages. This helped managers to be on time with loading customer purchase with increased temporal immediacy. A real options investment space involving six spaces has also been developed in this conceptual work. These were Never Invest, Immediately Investment, Present-day Investment Possibility, Possibly Invest Later, Invest Probably Later and Possibly Never Invest.
Research limitations/implications
The foundations of this study domain encompassed work done by an eclectic mix of scholars like from technology management (Siggelkow and Terwiesch, 2019a; Porter and Heppelmann, 2014), real options (Trigeorgis and Reuer, 2017; Luehrman, 1998a, 1998b), marketing intelligence and planning (Appel et al. , 2020; Thaichon et al. , 2019; Thaichon et al. , 2020; Ye et al. , 2019) and strategy from a demand positioning school of thought (Adner and Zemsky, 2006).
Practical implications
The findings would help managers to comprehend what level of investments need to be done in a staggered manner. The phased way of investing towards the deployment of ML and AI-based persuasive technologies would enable better monetization of customer futures. This would aid marketing managers for increased customer engagement at the individual level, fast monetization of customer futures and increased number of customers and consumption on m-commerce apps and e-commerce sites.
Originality/value
This was one of the first studies to apply real options investment perspective towards the deployment of ML and AI-based persuasive technologies for monetizing customer futures.
Based on the psychological reactance, this study tries to explore the dark side and grey role of the personalization recommendation system of short-form video application in understanding the discontinuance behavior. Specifically, two major depressing consequences of the personalization recommendation system are proposed, namely, privacy concerns and perceived information narrowing. Specifically, personalization recommendation system of short-form video App has significant positive influence on both privacy concern and perceived information narrowing. Besides, the empirical study shows perceived information narrowing is positively related to psychological reactance. However, personalization recommendation system does not lead to discontinuous usage behavior through privacy concerns or perceived information narrowing. Although personalization recommendation has not an indirect effect on discontinuous usage behavior, personalization recommendation has a potential risk to create psychological pressure on users, making personalized recommendations counterproductive. This study renders new insights on the dark side of the personalization recommendation system and provides practical suggestions for short-form video application providers.
