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Because of modern digital technologies, business environments are turning into digital ecosystems, wherein a firm’s traditional interdependencies are increasingly influenced by digital connectivity. For legacy firms, or firms yet to incorporate these technologies into their business models, this shift ushers in new opportunities for value creation, albeit through new capabilities. In this article, we focus on how legacy firms can embrace digital ecosystems to create value through a new capability: digital customer orientation. We define digital customer orientation as offering customized and enriched customer experiences made possible by embracing digital ecosystems. We develop a framework for legacy firms to develop capabilities for digital customer orientation in three steps: (1) by distilling key insights on how digital natives such as Amazon, Google and Facebook leverage their digital ecosystems for digital customer orientation; (2) by showing how legacy firms can apply those insights to harness their digital ecosystems and develop their own approaches for digital customer orientation; and (3) by offering a road map and a research agenda for legacy firms to engage in digital customer orientation—both by highlighting the organizational attributes needed, and by framing those attributes within the principles of transformative marketing.

[Display omitted] •Conceptual overview of ML and demonstrate its valuable strengths in data analysis.•Impact of ML on retailing with emphasis on metrics and analytics.•ML can be transformative for both retailing researchers and practitioners.•New opportunities to improve customer experience. This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.

Strategic management scholars have long explored the broad topic of innovation, a cornerstone in creating competitive advantage. Any attempt at theory construction in this area must encompass reliable and valid measures for key innovation characteristics. Yet, with respect to an important construct, i.e., disruptiveness of innovations, there has been relatively little academic research. Without formalizing the disruptiveness concept with a reliable and valid measure, it is difficult to conduct rigorous research to uncover the causes of the innovator's dilemma and identify mechanisms to help incumbents develop such innovations. In this paper, we develop a scale for the disruptiveness of innovations. We collected data from senior executives (vice president or general manager level) at 199 strategic business units (SBUs) in 38 Fortune 500 corporations and performed a series of analyses to establish the reliability and validity of the disruptiveness scale. The reliability measures, exploratory factor analysis, confirmatory factor analysis, and subsequent statistical tests strongly support our measure. Further, we also present nomological validity of the disruptiveness construct, thus establishing its predictive validity. Thus, this paper distinguishes the disruptiveness concept from other established innovation constructs, such as radicalness and competency destroying. Finally, we discuss the significance of our results and how this study might be useful to other researchers.

We estimate the joint impact of the frequency reward and customer tier components of a loyalty program on customer behavior and resultant sales. We provide an integrated analysis of a loyalty program incorporating customers' purchase and cash-in decisions, points pressure and rewarded behavior effects, heterogeneity, and forward-looking behavior. We focus on four key research questions: (1) How important is it to combine both components in one model? (2) Does points pressure exist in the context of a two-component loyalty program? (3) How is the market segmented in its response to the combined program? (4) Do the programs complement each other in terms of the incremental sales they produce? Our most basic message is that the frequency reward and customer tier components of loyalty programs should be modeled jointly rather than in separate models. We find strong evidence for points pressure for both the customer tier and frequency reward components using both model-based and model-free evidence. We find a two-segment solution revealing a \"service-oriented\" segment that highly values cash-ins for room upgrades and staying in \"luxury\" hotels, and a \"price-oriented\" segment that is more price sensitive and highly values the frequency reward aspects of the loyalty program. Furthermore, we find that both components generate incremental sales. Also, there was slight synergy between the programs but not a huge amount. Overall, each component contributes to increased revenues and does not interfere with the other.

In the customer expectations arena, relatively little attention has been paid to the impact on expectations of variation in cultural variables unique to a country. Here we focus on one country, India, and a major cultural influence there—the extent of belief in karma. Prior research in the United States suggests that disconfirmation sensitivity lowers expectations. Here we examine whether belief in karma and, consequently, having a long‐term orientation, counteracts the tendency to lower expectations in two studies that measure and prime respondents’ belief in karma. Results show that the extent of belief in karma, operating largely through its impact on long‐run orientation, does moderate (decrease) the effect of disconfirmation sensitivity on expectations. These findings suggest that it is important to tailor advertising messages by matching them with customer expectations and their cultural determinants.

Dynamic pricing has evolved with technology from earlier price negotiations. To maximize revenue and provide specialized shopping experiences, businesses today use algorithms and data analysis to adapt prices. We define dynamic pricing as price changes that are prompted by changes or differences in four key underlying market demand drivers: (1) People (i.e., individual consumers or consumer segments), (2) Product configurations, (3) Periods (i.e., time), and (4) Places (i.e., locations). The transition from static pricing (uniform prices) to dynamic pricing (changing prices) is evident from different examples, such as online retailers personalizing offers based on customer behavior, and algorithms using facial recognition for personalized pricing in physical stores. Fueled by AI and machine learning algorithms, dynamic pricing is transforming industries from transportation to e-commerce, optimizing revenue and enhancing customer experiences. While it offers benefits like personalization, challenges include ethical concerns, cost of implementation, and customer dissatisfaction. Effective data organization and AI expertise are crucial, but potential pitfalls and regulatory oversight must also be considered. This paper examines the multidimensional application of dynamic pricing, highlights the adaptability and efficiency of dynamic pricing in forming profitable pricing strategies and maximizing revenue, and calls for continued research on the topic to balance revenue, customer satisfaction, and ethics.

While much research has emphasized improving current new product concept techniques, little work has focused on trait-based approaches that specify which consumers are the \"right\" ones to use in the new product development process, particularly in the consumer goods industry. The authors propose that the right consumers to use possess what they call an \"emergent nature,\" defined as the unique capability to imagine or envision how concepts might be developed so that they will be successful in the mainstream marketplace. The authors draw on research on personality theory and information-processing styles to support their conceptualization and develop and validate a highly reliable scale to measure emergent nature (Study 1). In subsequent multipart studies, they show in both group (Studies 2a–2c) and individual (Studies 3a and 3b) settings across two distinct product categories that consumers high in emergent nature are able to develop product concepts that mainstream consumers find significantly more appealing and useful than concepts developed by typical, lead user, or even innovative consumers.

•Artificial intelligence (AI) will substantially impact retailing.•AI adoption contingent on factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns.•Near-term impact of AI on retailing is substantial, but may not be as pronounced as the popular press might suggest. Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.