1-13 of 13 Results  for:

  • Information Systems x
Clear all


Coordinating Knowledge: A New Lens to Understanding the Role of Technology in Episodic Coordination  

Elena Karahanna and Jennifer Claggett

Previous research in coordination lacked a practical explication of the metaknowledge used to enact coordination, which is particularly problematic as more coordination processes become (or attempt to become) digitized. One can better understand this meta knowledge by focusing on the coordination episode. The authors of this article define coordinating knowledge as knowledge that facilitates the exchange of information between two or more actors in order to achieve a shared goal by guiding (a) the timing, (b) the selection of actors, (c) the content, and (d) the method of the exchange. By integrating four bodies of literature (structured mechanisms, domain expertise, team familiarity, and transactive memory systems) that provide important insights into coordination the authors anatomize the framework into 14 specific types of coordinating knowledge that can impact how a coordination episode is enacted and its outcomes. Specifically, coordinating knowledge about triggers refers to knowledge indicating a need to initiate a coordination episode and may take the form of time-scheduled triggers, event-sequence triggers, and emergent triggers. Coordinating knowledge about actors refers to knowledge that helps select with whom to coordinate and may take the form of role, assignment, or individual knowledge about actors. Coordinating knowledge about content refers to knowledge that either helps select or present content shared during the coordination episode and may take the form of predetermined content selection or presentation, emergent content selection, recipient-tailored content selection, and shared understanding. Finally, coordinating knowledge about method refers to knowledge that helps select the appropriate medium of communication for a coordination episode and may take the form of predetermined method selection, media-fit method selection, or recipient-tailored method selection. Coordinating knowledge is conceptualized as a profile construct with meaningful combinations of coordinating knowledge that can be used to address different coordination dependencies and other contingencies. This conceptual framework affords a new understanding of how coordination is enacted and opens avenues to future research to explore how the presence and utilization of specific types of coordinating knowledge are likely to impact coordination performance. By explicating and elaborating upon coordinating knowledge, scholars and practitioners will be better positioned to design information systems to aid in the exchange of information by embedding different types of coordinating knowledge. Thus, the coordinating knowledge lens will be useful in understanding the evolving role of technology in coordination processes.


The Economics of Hacking  

Kai-Lung Hui and Jiali Zhou

Hacking is becoming more common and dangerous. The challenge of dealing with hacking often comes from the fact that much of our wisdom about conventional crime cannot be directly applied to understand hacking behavior. Against this backdrop, hacking studies are reviewed in view of the new features of cybercrime and how these features affect the application of the classical economic theory of crime in the cyberspace. Most findings of hacking studies can be interpreted with a parsimonious demand-and-supply framework. Hackers decide whether and how much to “supply” hacking by calculating the return on hacking over other opportunities. Defenders optimally tolerate some level of hacking risks because defense is costly. This tolerance can be interpreted as an indirect “demand” for hacking. Variations in law enforcement, hacking benefits, hacking costs, legal alternatives, private defense, and the dual-use problem can variously affect the supply or demand for hacking, and in turn the equilibrium amount of hacking in the market. Overall, it is suggested that the classical economic theory of crime remains a powerful framework to explain hacking behaviors. However, the application of this theory calls for considerations of different assumptions and driving forces, such as psychological motives and economies of scale in offenses, that are often less prevalent in conventional (offline) criminal behaviors but that tend to underscore hacking in the cyberspace.


E-Learning, Information Technology, and Student Success in Higher Education  

Kim Cliett Long

E-learning expands options for teaching and learning using technology. This nomenclature has been solidly in use for the last ten years. The expansive and ever fertile frontier of e-learning—a term used interchangeably with distance and online learning—has become standard fare as an educational delivery solution designed to enhance knowledge and performance. Many educational institutions, corporate enterprises and other entities are utilizing web-based teaching and learning methodologies to deliver education either partially or wholly online using electronic platforms. The learning value chain, including management and delivery, has created multimodal systems, content, and processes to increase accessibility, measurability, and cost effectiveness by infusing advanced learning techniques, such as adaptive learning or communities of practice, among students, employee groups, and lifelong learners. It is interesting to note that e-learning encapsulates internet based courseware and all other asynchronous and synchronous learning, as well as other capabilities for supporting learning experiences. Student success and advancements in technology are now inextricably linked as a result of higher education institutions embracing and offering e-learning options. The absence of direct instructor guidance makes distance learning particularly difficult for some students. Certain students struggle with the lack of guidance inherent in online learning and the requisite need to work independently. In particular, the lack of high touch strategies in e-learning often leads students to drop or fail courses. While some students struggle to remain engaged in technology-enabled learning, technology is often the vehicle for keeping these same students on task. There are a variety of electronic tools designed to augment online learning and keep online learners on task. Podcasts, for example, can be easily downloaded, then played back on a student’s media player or mobile device at a later date. The student is not tied to a computer, which results in a more comprehensive learning experience. In many cases, e-learning has become a very lucrative and desirable marketplace for higher education institutions. The business case for e-learning is a clarion call for tight integration among business, human resources, and knowledge and performance management. Hence, it is incumbent upon educational institutions to instill approaches that focus on the learner, learning, and improved performance, more so than the tools and technology. Of further importance is the need for higher education institutions to provide stratagems for developing and supporting caring online relationships, individualized student environments, collaboration, communication, and e-learning culture. Ultimately, institutions should measure not only improved business and performance, but also improved student online learning aptitudes (more self-motivated, self-directed, and self-assessed learning).


External Enablers of Entrepreneurship  

Per Davidsson, Jan Recker, and Frederik von Briel

“External enabler” (EE) denotes nontrivial changes to the business environment—such as new technology, regulatory change, demographic and sociocultural trends, macroeconomic swings, and changes to the natural environment—that enable entrepreneurial pursuits. The EE framework was developed to increase knowledge accumulation in entrepreneurship and strategy research regarding the influence of environmental factors on entrepreneurial endeavors. The framework provides detailed structure and carefully defined terminology to describe, analyze, and explain the influence of changes in the business environment on entrepreneurial pursuits. EE characteristics specify the environmental changes’ range of impact in terms of spatial, sectoral, sociocultural, and temporal scope as well as the degree of suddenness and predictability of their onset. EE mechanisms specify the types of benefits individual ventures may derive from EEs. Among others, these include cost saving, resource provision, making possible new or improved products/services, and demand expansion. EE roles situate these (anticipated) mechanisms in entrepreneurial processes as triggering and/or shaping and/or outcome-enhancing. EE’s influence is conceived of as mediated by entrepreneurial agency that—in addition to agent characteristics—is contingent on the opacity (difficulty to identify) and agency-intensity (difficulty to exploit) of EE mechanisms, with the ensuing enablement being variously fortuitous or resulting from strategic deliberation.


From Decision Making to Decision Support  

Frederic Adam

In such a complex and well-researched domain as decision support systems (DSS), with a long history of authors making insightful contributions since the 1960’s, it is critical for researchers, especially those less experienced, to have a broad knowledge of the seminal work that has been carried out by prior generations of researchers. This can serve to avoid proposing research questions which have been considered many times before, without having consideration for the answers which have been put forward by previous scholars, thereby reinventing the wheel or “rediscovering” findings about the life of organizations that have been presented long before. The study of human and managerial decision-making is also characterized by considerable depth and seminal research going back to the beginning of the 20th century, across a variety of fields of research including psychology, social psychology, sociology or indeed operations research. Inasmuch as decision-making and decision support are inextricably linked, it is essential for researchers in DSS to be very familiar with both stream of research in their full diversity so they are able to understand both what activity is being supported and how to analyze requirements for developing decision support artefacts. In addition, whilst the area of decision support has sometimes been characterized by technology-based hype, it is critical to recognize that only a clear focus on the thinking and actions of managers can provide decisive directions for research on their decision support needs. In this article, we consider first the characteristics of human cognition, before concentrating on the decision-making needs of managers and the lessons that can be derived for the development of DSS.


From Decision Support to Analytics  

Ciara Heavin and Frederic Adam

Since the 1960s, information technology (IT)/information systems (IS) professionals, data practitioners, and senior managers have focused on developing decision support capabilities to enhance organizational decision making. Initially, this quest was mostly driven by successive generations of technological advances. However, in the last decade, the pace at which large volumes of diverse data can be collected and processed, new algorithmic advances, and the development of computational infrastructure such as graphics processing units (GPUs) and tensor processing units (TPUs) have created new opportunities for global businesses in areas such as financial services, manufacturing, retail, sports, and healthcare. At this point, it seems that most industries and public services could potentially be revolutionized by these new techniques. The word analytics has replaced the previous individual components of computerized decision support technologies that have been developed under various labels in the past (). Much of the traditional researcher and practitioner communities who were concerned with decision support, decision support systems (DSSs), and business intelligence (BI) have reoriented their attention to innovative tools and technologies to derive value from new data streams through artificial intelligence (AI) and analytics. Identifying the main areas of focus for decision support and analytics provides a stimulus for new ideas for researchers, managers, and IS/IT and data professionals. These stakeholders need to undertake new empirical studies that explain how analytics can be used to develop and enhance new forms of decision support while considering the dilemmas that may arise due to the data capture and analyses of new digital data streams.


Geographic Information Systems and Location Analytics for Business and Management  

Peter Keenan

A geographic information system (GIS) is a system designed to capture, store, organize, and present spatial data, which is referenced to locations on the Earth. Locational information is of value for a wide range of human activities for decision-making relating to these activities. As spatial data is relatively complex, GIS represents a challenging computer application that has developed later than some other forms of computer systems. GIS uses spatial data for a region of the Earth; such regional data are of interest to a wide range of users whose activities take place in that region, and so many users in otherwise disconnected domains share spatial data. The availability and cost of spatial data are important drivers of GIS use, and the sourcing and integration of spatial data are continuing research concerns. GIS use now spans a wide range of disciplines, and the diversity created is one of the obstacles to a well-integrated research field. Location analysis is the use of GIS for general-purpose analysis to determine the preferred geographic placement of human activities. Location analytics uses spatial data and quantitative spatial models to support decision-making, including location analysis. The growth of location analytics reflects the increasing amounts of data now available owing to new data collection technologies such as drones and because of the massive amounts of data collected by the use of mobile devices like smartphones. Location analytics allow many valuable new services that play an important role in new developments such as smart cities. Location analytics techniques potentially allow the tracking of individuals, and this raises many ethical questions, however useful the service provided; therefore, issues related to privacy are of increasing concern to researchers.


Governance in Digital Open Innovation Platforms  

Likoebe Maruping and Yukun Yang

Open innovation is defined as an approach to innovation that encourages a broad range of participants to engage in the process of identifying, creating, and deploying novel products or services. It is open in the sense that there is little to no restriction on who can participate in the innovation process. Open innovation has attracted a substantial amount of research and widespread adoption by individuals and commercial, nonprofit, and government organizations. This is attributable to three main factors. First, open innovation does not restrict who can participate in the innovation process, which broadens the access to participants and expertise. Second, to realize participants’ ideas, open innovation harnesses the power of crowds who are normally users of the product or service, which enhances the quality of innovative output. Third, open innovation often leverages digital platforms as a supporting technology, which helps entities scale up their business. Recent years have witnessed a rise in the emergence of a number of digital platforms to support various open innovation activities. Some platforms achieve notable success in continuously generating innovations (e.g., InnoCentive.com, GitHub), while others fail or experience a mass exodus of participants (e.g., MyStarbucksIdea.com, Sidecar). Prior commentaries have conducted postmortems to diagnose the failures, identifying possible reasons, such as overcharging one side of the market, failing to develop trust with users, and inappropriate timing of market entry. At the root of these and other challenges that digital platforms face in open innovation is the issue of governance. In the article, governance is conceptualized as the structures determining how rigidly authority is exerted and who has authority to make decisions and craft rules for orchestrating key activities. Unfortunately, there is no comprehensive framework for understanding governance as applied to open innovation that takes place on digital platforms. A governance perspective can lend insight on the structure of how open innovation activities on digital platforms are governed in creating and capturing value from these activities, attracting and matching participants with problems or solutions, and monitoring and controlling the innovation process. To unpack the mystery of open innovation governance, we propose a framework by synthesizing and integrating accreted knowledge from the platform governance literature that has been published in prominent journals over the past 10 years. Our framework is built around four key considerations for governance in open innovation: platform model (firm-owned, market, or community), innovation output ownership (platform-owned, pass-through, or shared), innovation engagement model (transactional, collaborative, or embedded), and nature of innovation output (idea or artifact). Further, we reveal promising research avenues on the governance of digital open innovation platforms.


Information Technology Project Risk as a Dynamic Phenomenon  

Mazen El-Masri and Suzanne Rivard

Information systems (IS) research provides strong evidence for the effect of information technology (IT) project risk and on-project failure. However, no consensus has yet been reached on what constitutes risk and how it should be specified. Existing definitions of the risk construct are diverse leading to fragmented scientific knowledge. This article specifies IT project residual risk as an aggregate multidimensional construct comprised of four dimensions: project sources, undesirable events, risk management mechanisms, and expected outcomes. The construct accentuates the dynamic nature of IT project risk and can help reorganize the abundant risk factors found in the IS literature under its four dimensions while exposing their interactions.


Innovation Challenges  

Yao Sun and Ann Majchrzak

Starting from early 21st century, companies increasingly use open innovation challenges to generate creative solutions to business problems. This revolution in business models and management strategy reflects the evolution supported by new technology. Employing this new strategic model, companies seek to innovate in a wide variety of areas, such as clothes designs, photography solutions, business plans, and film production. Contrary to closed innovation through which companies develop creative ideas internally, innovation challenges are catalyzed by socioeconomic changes such as the rapid advancement of information technologies, increased labor division, as well as ever-expanding globalization. Going hand in hand are trends such as outsourcing, occurring in parallel in the management area, which makes companies more agile and flexible. Multifaceted and multidimensional, open innovation challenges consist of various activities such as inbound innovation (acquiring and sourcing), outbound innovation (selling and revealing), or a compound mix of these two forms. It also pertains to complementary assets, absorptive capacity, organizational exploration, and exploitation. In an attempt to determine how to best support such an important component of society, scholars and practitioners continue to pursue effective innovation challenge architecture (the art or practice that guides participants’ interactions and exchange) that allows open collaboration among the crowd, as well as an approach for incorporating such architecture into technological platforms in order to improve the crowd’s creativity. This issue is addressed by focusing on existing research that delineates various types of effective architecture of innovation challenges. A theory-based framework guides this examination, and work from various scholarly perspectives of innovation challenges, knowledge management, motivated knowledge sharing, and crowdsourcing are integrated into this framework.


Innovation in Artificial Intelligence: Illustrations in Academia, Apparel, and the Arts  

Andreas Kaplan

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.


Platformizing Organizations: A Synthesis of the Literature  

Pankaj Setia, Franck Soh, and Kailing Deng

Organizations are widely building digital platforms to transform operations. Digital platforms represent a new way of organizing, as they leverage technology to interconnect providers and consumers. Using digital technologies, organizations are platformizing operations, as they open their rigid and closed boundaries by interconnecting providers and consumers through advanced application programming interfaces (APIs). Early research examined platformized development of technology products, with software development companies—such as Mozilla Foundation—leading the way. However, contemporary organizations are platformizing nontechnology offerings (e.g., ride-sharing or food delivery). With growing interest in platforms, the basic tenets underlying platformization are still not clear. This article synthesizes previous literature examining platforms, with the aim of examining what platformization is and how and why organizations platformize.


Virtual Worlds Affordances for Organizations  

Kathryn Aten

Virtual work has become critical to competing in the global information economy for many organizations. Successfully working through technology across time and space, especially on collaborative tasks, however, remains challenging. Virtual work can lead to feelings of isolation, communication and coordination difficulties, and decreased innovation. Researchers attribute many of these challenges to a lack of common ground. Virtual worlds, one type of virtualization technology, offer a potentially promising solution. Despite initial interest, organizational adoption of virtual worlds has been slower than researchers and proponents expected. The challenges of virtual work, however, remain, and research has identified virtual world technology affordances that can support virtual collaboration. Virtual world features such as multi-user voice and chat, persistence, avatars, and three-dimensional environment afford, in particular, social actions associated with successful collaboration. This suggests that the greatest value virtual worlds may offer to organizations is their potential to support virtual collaboration. Organizational scholars increasingly use a technology affordance lens to examine how features of malleable communication technologies influence organizational behavior and outcomes. Technology affordances represent possibilities of action enabled by technology features or combinations of features. Particularly relevant to virtual world technology are social affordances—affordances of social mediating technologies that support users’ social and psychological needs. To be useful to organizations, there must be a match between virtual world technology affordances, organizational practices, and a technology frame or organizing vision. Recent studies suggest a growing appreciation of the influence of physical organizational spaces on individual and organizational outcomes and increasing awareness of the need for virtual intelligence in individuals. This appreciation provides a possible basis for an emerging organizing vision that, along with recent technology developments and societal comfort with virtual environments, may support wider organizational adoption of virtual worlds and other virtualization technologies.