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The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.

Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a venture’s user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.

Robotic handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of automated and robotic handling systems, such as shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and operations research modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Because of unique system features (such as autonomous control, flexible layout, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotic warehouse systems will form the next category of warehouses. All vital warehouse design, planning, and control logic, such as methods to design layout, storage and order-picking system selection, storage slotting, order batching, picker routing, and picker to order assignment, will have to be revisited for new robotized warehouses.

Advertisers need to optimize their efforts on social networks to engage consumers effectively. Existing literature on this topic has not yet explained how social network advertising (SNA) can be categorized into different content types and how to conceptualize and operationalize digital consumer engagement (DCE) in social networks. Thus, we derive seven content categories for social network advertising and a four-level model for DCE based on consumers' intermediate mind-set responses. We propose the impact of different SNA categories as an antecedent of DCE. Our results confirm a significant but unequal impact of at least four content categories on various engagement metrics. We therefore distill the successful content strategies and content attributes for specific types of engagement and confirm intermediate responses to advertising in a real market situation.

Digital transformation, a term introduced to talk about the various changes in business and society due to the increased usage of digital technologies, has recently gained much attention both in research and in practice. However, an analysis of 41 digital transformation frameworks following a developmental literature review shows that several areas can be expanded upon. We propose a novel framework that deals with the underrepresented areas by consolidating the various concepts found in the literature, explicitly including the role of society, highlighting the evolution over time, and including the drivers of digital transformation that we classified into 23 ‘digital transformation interactions’ across six categories. This novel perspective contributes to our macro-understanding of digital transformation and can be used as a lens for further research to generate fresh insights into unanswered research avenues. Ultimately, this paper can be the first step towards a unified understanding of digital transformation.

Although digital assistants with humanlike features have become prevalent in computer games, few marketing studies have demonstrated the psychological mechanisms underlying consumers’ reactions to digital assistants and their subsequent influence on consumers’ game enjoyment. To fill this gap, the current study examined the effect of anthropomorphic representations of computerized helpers in computer games on game enjoyment. In the current research, consumers enjoyed a computer game less when they received assistance from a computerized helper imbued with humanlike features than from a helper construed as a mindless entity. We offer a novel mechanism that the presence of an anthropomorphized helper can undermine individuals’ perceived autonomy during a computer game. Across six experiments, we show that the presence of an anthropomorphized helper reduced game enjoyment across three different games. By measuring participants’ perceived autonomy (study 1) and employing moderators such as importance of autonomy (studies 2, 3, and 4), we also provide evidence that the reduced feeling of autonomy serves as the mechanism underlying the backfiring effect. Finally, we demonstrate that the effect of anthropomorphism on game enjoyment can be extended to other game-related outcomes, such as individuals’ motivation to persist in the game (studies 4 and 5).

A significant gap exists in the conceptualization of entrepreneurship in the digital age. This paper introduces a conceptual framework for studying entrepreneurship in the digital age by integrating two wellestablished concepts: the digital ecosystem and the entrepreneurial ecosystem. The integration of these two ecosystems helps us better understand the interactions of agents and users that incorporate insights of consumers' individual and social behavior. The Digital Entrepreneurial Ecosystem framework consists of four concepts: digital infrastructure governance, digital user citizenship, digital entrepreneurship, and digital marketplace. The paper develops propositions for each of the four concepts and provides a theoretical framework of multisided platforms to better understand the digital entrepreneurial ecosystem. Finally, it outlines a new research agenda to fill the gap in our understanding of entrepreneurship in the digital age.

Increasingly abundant trace data provide an opportunity for information systems researchers to generate new theory. In this research commentary, we draw on the largely “manual” tradition of the grounded theory methodology and the highly “automated” process of computational theory discovery in the sciences to develop a general approach to computationally intensive theory development from trace data. This approach involves the iterative application of four general processes: sampling, synchronic analysis, lexical framing, and diachronic analysis. We provide examples from recent research in information systems.

Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that machine learning holds promise for understanding the process by which governance structures are chosen and has potential to help real-world firms improve their governance.