Search

This article analyzes the nature and temporal change of conceptual contributions in marketing scholarship with two complementary studies. First, based on a computer-aided text analysis of 5,922 articles published in the four major marketing journals between 1990 and 2021, the authors analyze how conceptual contributions have changed over time using the MacInnis (2011) framework. Results indicate that over the past three decades, theorizing efforts have strongly favored "envisioning" and "explicating" at the expense of "relating" and "debating," with this imbalance increasing over time. Second, the authors draw on 48 in-depth interviews with editors, department heads, and authors to validate these patterns and uncover the underlying mechanisms. The findings indicate that a prevalent thought style has developed in the field—defined by the research ideals of novelty, clarity, and quantification—that shapes the collective view of how marketing scholars, in their roles as authors, reviewers, and mentors, can make a valuable contribution to marketing scholarship. This thought style favors envisioning and explicating contributions and disfavors relating and debating contributions. Jointly, the two studies point to several rebalancing options that can reinvigorate relating and debating contributions while preserving the current strengths of the marketing field. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is increasingly transforming the way we work, live, and travel. IoT devices collect, store, analyze, and act upon a continuous stream of data as a by-product of everyday use. However, IoT devices need unrestricted data access to fully function. As such, they invade users' virtual and physical space and raise far-reaching privacy challenges that are unlike those examined in other contexts. As advanced IoT devices, connected cars offer a unique setting to review and extend established theory and evidence on privacy and data sharing. Employing a sequential mixed methods design, we conducted an interview study (n=120), a survey study (n=333), and a field experiment (n=324) among car drivers to develop and validate a contextualized model of individuals' data sharing decisions. Our findings from the three studies highlight the interplay between virtual and physical risks in shaping drivers' privacy concerns and data sharing decisions-with information privacy and data security emerging as discrete yet closely interrelated concepts. Our findings also highlight the importance of psychological ownership, conceptualized as drivers' feelings of possession toward their driving data, as an important addition to established privacy calculus models of data sharing. This novel perspective explains why individuals are reluctant to share even low-sensitivity data that do not raise privacy concerns. The psychological ownership perspective has implications for designing incentives for data-enabled services in ways that augment drivers' self-efficacy and psychological ownership and thereby encourage them to share driving data. These insights help reconcile a fundamental tension among IoT users-how to avail the benefits of dataenabled IoT devices while reducing the psychological costs associated with the sharing of personal data. [ABSTRACT FROM AUTHOR]

How do employees respond to organizational identity threats? Despite its theoretical and practical importance, this question has had surprisingly little research devoted to it. In particular, evidence remains unclear as to whether organizational identity threats lead employees to disidentify with their organization (to protect their individual identities by distancing themselves from the organization) or, in contrast, whether they foster organizational identification and cohesion (to protect the identity of the organization as a whole by rallying around and defending the organization). Based on the integration of social identity theory and attribution theory, we develop a conceptual model that clarifies the process by which employees recognize organizational identity threats as challenging their individual identity, make sense of their underlying causes, and decide how to respond to them. [ABSTRACT FROM AUTHOR]

The substantial volume, continued growth, and resulting complexity of the scientific literature not only increases the need for systematic, replicable, and rigorous literature reviews, but also highlights the natural limits of human researchers' information processing capabilities. In search of a solution to this dilemma, computational techniques are beginning to support human researchers in synthesizing large bodies of literature. However, actionable methodological guidance on how to design, conduct, and document such computationally augmented literature reviews is lacking to date. We respond by introducing and defining computational literature reviews (CLRs) as a new review method and put forward a six-step roadmap, covering the CLR process from identifying the review objectives to selecting algorithms and reporting findings. We make the CLR method accessible to novice and expert users alike by identifying critical design decisions and typical challenges for each step and provide practical guidelines for tailoring the CLR method to four conceptual review goals. As such, we present CLRs as a literature review method where the choice, design, and implementation of a CLR are guided by specific review objectives, methodological capabilities, and resource constraints of the human researcher. [ABSTRACT FROM AUTHOR]