Companies, public authorities, and private Internet users are increasingly confronted with cyber risks. However, research and practice have focused particularly on cyber security in an organizational context. This ignores the fact that private individuals face cyber risks that differ from those they encounter as part of their job profile in an organizational context. Moreover, it is evident that private individuals behave differently concerning cyber risks, which can be explained by different context-dependent threat awareness as part of multi-dimensional cyber security competencies. Therefore, we extend the Cyber Security Domain Model (CSDM), which was initially established as the prerequisite for building cyber security competencies related to threats within an organizational context, to individuals. We draw on practitioner as well as academic sources to adjust and refine the two dimensions of (1) threat area and (2) threat event in the context of individuals, using a structured literature review.
With the Internet’s expansion, various online platforms have arisen, allowing people to express their views and influence electronic word-of-mouth (eWOM) for various brands. Depending on their personalities, some Internet users may express dissatisfaction or exhibit anti-brand behaviors when they receive products or services that don’t meet their expectations. Not all users, however, come to the brand’s defense, even if they disagree with the critics or anti-brand behaviors. Through a case study using netnography, we explored the influence of personality on online brand defense. We found that individuals seeking social recognition and self-validation are more likely to engage in online brand defense. Recognizing the impact of personality on online behavior can assist businesses and marketers in formulating strategies for fostering positive interactions with consumers.
The 21st century has earned the tragic sobriquet “The Century of Disasters” due to the escalating frequency, complexity, and severity of both natural and human-made calamities. Climate change, marked by an increasing frequency and severity of natural disasters, necessitates a paradigm shift for businesses operations, urging a transition from reactive to proactive and resilient strategies. Research in this area has become crucial, offering organizations vital insights to better prepare for and mitigate the impacts of future disasters. This chapter delves into the multifaceted impact of natural disasters on businesses, exploring how different disciplines are rising to meet these unique challenges. Key findings emphasize the need for flexible supply chains, robust disaster preparedness, and innovative financing mechanisms to mitigate risks and ensure business continuity. As the business landscape becomes increasingly shaped by the realities of disaster risk, it is clear that an interdisciplinary approach is essential. This chapter offers insights from the convergence of literature from Operations Management, Finance, Marketing, and Information Systems. Together, these findings offer a comprehensive framework for understanding and mitigating the complex challenges that disasters present. Pertinently, this chapter explores the burgeoning field of IS in disaster management, highlighting its critical role in saving lives, protecting assets, and building more resilient communities. While this discourse demonstrates the growing importance of IS in disaster management, a number of critical questions remain unanswered. These pave the way for future research opportunities, which are presented at the conclusion of this chapter.
The past few years have witnessed a rapid evolution in artificial intelligence (AI), marking the advent of a new cognitive era. Concurrently, academic interest in the organizational adoption and impacts of AI has surged, reflected in a diverse array of research across management disciplines. This chapter presents the Cognition Cube, a theoretical framework designed to synthesize emerging thematic trends in literature concerning AI’s firm-level impacts. Organized into temporal evaluations, foundational definitions of intelligent, agentic, and cognitive systems, and exemplar studies within the cube’s dimensions of AI, Human, and Task, this framework categorizes existing research and guides future inquiries. Highlighting areas such as cognitive reapportionment, GREAT economies, and innovation, the chapter aims to stimulate further research, anticipating significant growth in the study of AI’s organizational implications as we advance into the cognitive age.
This research-in-progress paper investigates the impact of Large Language Models (LLMs) on Digital Transformation (DT) across various industries, highlighting a paradigm shift in software customization and integration. Utilizing a mixed-methods approach, the study combines qualitative case studies and Qualitative Comparative Analysis (QCA) to explore the transformative effects of LLMs in business environments. Key findings reveal that LLMs significantly enhance customization capabilities, streamline data integration, and promote agile decision-making processes. This transition marks a critical evolution in DT strategies, offering new opportunities for operational efficiency and competitive advantage. The paper contributes vital insights to both academic discourse in technology management and practical business strategy, emphasizing the increasingly central role of LLMs in the digital-first business landscape.
Earnings Call Transcripts are a valued data source across the fields of accounting, finance, strategic management, corporate governance, and corporate social responsibility. Researchers in the field of information systems have asserted the importance of ECT as a source and resource to understand firms’ digital endowments and strategies. The objective of this chapter is to provide a description and instructive guide for extracting digital and IT-related constructs of firms using natural language processing on ECTs. We explore the implementation of the text-mining and natural language processing techniques of bag-of-words, word2vec, lexical, semantic, and combined analyses to interpret a firm’s proclivity towards the utilization of digital and information technology. We discuss the strengths and limitations of employing ECT. Finally, we provide an extensive explication of alternative approaches and avenues for future research related to the use of earnings call transcripts in the field of information systems.
While cryptocurrencies gain popularity, many lose value over time. To harness the benefits of launching a new cryptocurrency, identifying factors influencing its perceived value is crucial. Our in-progress study, based on the Technology-Organisation-Environment (TOE) framework and trust-related literature, revealed that (1) security, transaction speed, supply, gifting, trust, user critical mass, and issuer interaction impact cryptocurrency value directly or indirectly, (2) Technical aspects matter less compared to organizational and environmental factors, (3) Drivers of value differ slightly between stablecoins and non-stablecoins, (4) Some factors influence value indirectly through trust, with no significant direct impact.
Small and Medium Enterprises [SMEs] have been contributing 30.27 percent to the gross domestic product and 49
In the evolving landscape of machine learning (ML), the explainability of complex models remains a pivotal challenge, especially in bridging the understanding between technical experts and frontline staff. This paper explores the role of Generative AI (GenAI) in demystifying the intricacies of ML explainability methods, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Our investigation delves into the trust gap that often arises due to the technical nature of these methods and how GenAI can serve as a mediator in this scenario. By leveraging GenAI’s capabilities in synthesizing comprehensible visualizations and intuitive explanations, we propose a framework that enhances the interpretability of ML results for non-technical audiences. This framework not only facilitates better understanding but also fosters trust and collaborative decision-making among diverse stakeholders. Our findings indicate that GenAI can significantly contribute to the democratization of ML knowledge, thereby empowering frontline staff to engage more effectively with ML outputs. This research underscores the potential of GenAI as a transformative tool in making ML more accessible and trustworthy across various sectors.
The aim of this paper is to address the contribution of collaborative governance to digital government performance by adequately assessing the determinants of collaboration from the perspective of organisation capability in managing excessive innovation problems. Transformation is complex, requires a long change process and has specific implications for how governments collaborate and innovate to improve the quality of work performance and the value of public service. The central hypothesis of this work is that collaborative governance could improve performance and promote collaborative digital innovation in a government context. This paper explores the multiple influencing factors including the competency of an institution, innovation through dynamic capability, collaborative leadership, co-creation strategy and digital platform capability. Thus, bringing forward organisation adaptation and IT management perspectives on the relationship between collaborative governance and digital government performance.
Existing literature attempting to model the determinants of Bitcoin pricing has used a combination of economic, transactional, and technical factors as the independent variables. We seek to build on this body of knowledge by narrowing our investigation to the impact of Consumers’ Digital Focus, which we conceptualize as a multidimensional construct that constitutes the volume, sentiment, and engagement of social media mentions and web search trends on Bitcoin prices, volatility, and growth. Specifically, we hypothesize that Tweet volumes (mentions), impressions, positive tweet sentiment, and engagement (immediate and lagged effect) for regular and verified users and internet search volume positively impact Bitcoin prices. We also hypothesize that Tweets with negative sentiment have a counter effect. Our study uses primary data from Blockchain, Twitter, and Google Search to test these hypotheses. Regression analysis finds broad support for our research model. Overall, we uncover novel insights that challenge conventional thinking regarding engagement and sentiment and present fresh insights into the influence of web searches and Twitter impressions on Bitcoin pricing.
Online health misinformation has become a more significant concern in recent years, especially since the COVID-19 pandemic. This has led to a pressing need to reduce its negative impact, such as decreasing trust and readership on online media and increased likelihood of social instability during the pandemic. Focusing on healthcare misinformation, we propose a theoretical model that explains the internal and external factors that influence the ability to distinguish healthcare misinformation. This model will be tested in future research, and it is hoped that we can advance theoretical understanding of misinformation and provide practical implications for both online media outlets and governments.
We are in a digital economy, and the ever-increasing use of digital products in our daily lives has left us wanting a superlative customer experience [1, 2]. An engaging customer experience results from a conscious embodiment of several factors across multiple disciplines. In this paper, I set the stage for an interdisciplinary research program to study the interactional effects of two seminal constructs in management value co-creation and organisational routines. This research is critical for furthering our understanding of digital product design.
It is challenging to predict financial markets, but there have been continued efforts to develop improved prediction methods. With availability of high frequency market psychology data, and guided by design science principles, this research iteratively develops and comprehensively evaluates DeepPsych, a deep learning system that leverages market psychology data to gain prediction advantage. Using two convolutional sequence-to-sequence channels to extract local and temporal features from psychology and trading data separately, the system outperforms other leading machine learning and deep learning models in both machine learning metrics and economic values realized through trading strategy based on the prediction. This research contributes to both information systems design science through innovation in deep learning and finance by providing empirical evidence about the predictive power of high frequency market psychology data. The research also benefits practice by producing a validated Fintech artifact.
This article explores the transformative impact of Decentralized Finance (DeFI) on the financial ecosystem and its implications for Information Systems. As global financial regulators adapt to the rise of DeFI, a decentralized blockchain network facilitates financial transactions through smart contracts, disrupting traditional intermediaries such as banks and exchanges. The decentralized nature of DeFI offers competitive, contestable, composable, and non-custodial financial ecosystems, presenting opportunities for efficiency and cost reduction. To architect DeFI solutions effectively, understanding the technology's layers and factors influencing adoption is crucial. The article introduces the Three-Layered DeFI Stack Reference (DSR) Model, emphasizing the Settlement Layer, DLT Application Layer, and DeFI Compositions. Architectural decisions involving permissioned or permissionless blockchains, database choices, trust considerations, and transaction interactions are explored. Considering the infancy of research on DeFI in Information Systems, the article proposes a research agenda. It categorizes potential research into the design and deployment of DeFI systems and the adoption and implications of these systems. The study also highlights the need for investigating the relationship between the extensiveness, evolvability, and enabling attributes of DeFI technology (3Es) and its real-world value, vision, and viability (3Vs). The research agenda aims to contribute to the understanding of DeFI's impact on information systems, offering exciting prospects for exploration in emerging and global markets.
The Internet of Things (IoT) has become part of everyday life and recorded an increasing number of users. However, security concerns have been raised regardless of the many benefits of the technology. Especially for consumers in online shopping, it is difficult to distinguish between more and less safe products. One proposal is to carry a security label to help consumers know which digital products to trust. Prior research only analyzes the impact of such labels from a consumer's perspective (i.e., the impact of security labeling on online consumer behavior). We currently lack an understanding of a manufacturer's perspective. Therefore, we conduct a literature review to identify factors influencing the decision to adopt security labels.
Artificial intelligence (AI) has witnessed widespread adoption across various organizational domains, including transport & logistics, wherein its applications range from driver assistance to parcel sorting and inventory planning to many more. The paper systematically elucidates the landscape of the usage of AI in these domains and seeks to investigate and analyze its opportunities and challenges. It focuses on assessing the importance and significance of the utilization of intelligent and autonomous transport as well as cleaner transport modalities. The methodology employed in this report is grounded in exploratory and descriptive analysis, focusing predominantly on a thorough examination of existing literature, empirical scientific research publications, and prior and ongoing AI initiatives. The results thus meticulously present the various facets of the existing challenges and opportunities faced by organizations, activities, and individuals in the adoption of AI technology and systems in the Transport and logistics sectors.
This paper presents novel pathways for Information Systems (IS) research within the context of family business. As these enterprises navigate the impacts of digital transformation, understanding the intersection of IS and familial commerce has become imperative. Specifically, we address the following questions: (1) What are the key intersections between IS research questions and the nuanced context of family businesses? (2) How can IS researchers contribute to the understanding and advancement of family businesses within the context of India, and in GREAT (Growing, Rural, Eastern, Aspirational, and Transitional) economies more broadly? Our extensive literature review showcases emerging trends in family business research and lessons from history and practice that are valuable for family firms in GREAT nations such as India. We also review existing IS research on issues centered around the family business context. Underscoring unexplored research inquiries that resonate with insights from Indian business history, we advocate for further research at the crossroads of IS and family business, acknowledging the existing scarcity of scholarly contributions within this domain.
We present a meta-analysis of cloud computing research in information systems. The study includes 152 referenced journal articles published between January 2010 to June 2023. We take stock of the literature and the associated research themes, research frameworks, the employed research methodology, and the geographical distribution of the articles. This review provides holistic insights into trends in cloud computing research based on themes, frameworks, methodology, geographical focus, and future research directions. The results indicate that the extant literature tends to skew toward themes related to business issues, which is an indicator of the maturing and widespread use of cloud computing. This trend is evidenced in the more recent articles published between 2016 to 2023.
Software as a Service (SaaS) is a segment within the technology services industry that has shown continued resilience and strong growth. SaaS is software owned, delivered, and managed remotely by one or more providers. It offers value to businesses by reducing the total cost of ownership through complete outsourcing, leveraging data and analytics to understand customers better, and making data-driven solutions that enhance performance. SaaS is highly scalable and generates reliably recurring revenue, making it an attractive acquisition target for B2B SaaS players. SaaS is seen as a key enabler of business value and transformation. One of the key factors that enables organizations to extract value is the maturity of architectures and better integration of new technologies. Enterprises are looking at SaaS as an alternative to purchasing, installing, and maintaining modifiable off-the-shelf software packages, offering flexibility in pricing and modular usage of features. Although SaaS offerings have been around for more than a decade, firms are still learning to realize value from their co-existence. However, implementing SaaS solutions comes with challenges and risks. In this chapter, we look at these factors and put forward several propositions laying down the foundation for empirical and qualitative research to be conducted for validation of the hypothesis.
