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How Does Big Data Analytics Shape Human Heuristics Adaptation in Strategic Decision-Making? A Perspective of Environmental Uncertainty Contingencies
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Organizations increasingly use artificial intelligence (AI) to solve previously unexplored problems. While routine tasks can be automated, the intricate nature of exploratory tasks, such as
solving new problems, demands a hybrid approach that integrates human intelligence with AI. We argue that the outcomes of this human-AI collaboration are contingent on the processes employed
to combine human and artificial intelligence. Our model unpacks three hybrid problem-solving processes and their outcomes: Compared to human problem solving, autonomous search generates
more distant solutions, sequential search enables more local solutions, and interactive search promotes more recombinative ones. Collectively, these hybrid problem-solving processes broaden
the range of organizational search outcomes. We enrich the behavioral theory of the firm with a technology-conscious perspective of organizational problem solving that complements its
traditional human-centric perspective. Additionally, we contribute to the literature on AI in management by extending its scope from using predictive AI for routine tasks to generative AI applications for more exploratory tasks, such as search and problem solving.
This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their “donors” in a specific training environment based on generative adversarial networks. A trained clone can be used by a decision-maker as an additional resource for one’s own cognitive enhancement, as an autonomous representative, or even as a replacement when appropriate. The assumption regarding this approach is as follows: when someone was forced to leave a critical process because of, for example, sickness, or wanted to take care of several simultaneously running processes, then they would be more confident knowing that their autonomous digital representatives were as capable and predictable as their exact personal “copy”. The Pi-Mind agent was evaluated in a Ukrainian higher education environment and a military logistics laboratory. In this paper, in addition to describing the artefact, its expected utility, and its design process within different contexts, we include the corresponding proof of concept, proof of value, and proof of use.
Artificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgments. We know little, however, about how human-AI augmentation takes place in practice. Yet, gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major U.S. hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three) did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices—practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call unengaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literature on AI adoption in knowledge work.
Many would argue that artificial intelligence (AI) is not just a technology but represents a paradigmatic shift in the relationship between humans and machines. Much of the literature assumes that AI-powered practices are substantially different and profoundly changes organizational structures, communication, affordances, and ecosystems. However, research in AI is often fragmented and lacks clarity. While the Information Systems (IS) field can play a pivotal role in the emergence and use of AI, there is a need for a clear direction that specifies how IS can contribute and what are to be the key research themes and questions. This paper draws on a PDW at ICIS 2020 and the discussions that followed. It summarizes and synthesizes five decades of the impact of AI on organizational practices, providing views from various perspectives. It identifies weaknesses in the current AI literature as measured against conceptual clarity, theoretical glue, cumulative tradition, parsimony, and applicability. The paper concludes by identifying direct actions that the IS research community can undertake to address these issues. The final contribution is a next-step research agenda to guide AI research in the coming years.
We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.
Artificial intelligence (AI) technologies offer novel, distinctive opportunities and pose new significant challenges to organizations that set them apart from other forms of digital technologies. This article discusses the distinct effects of AI technologies in organizations, the tensions they raise and the opportunities they present for information systems (IS) research. We explore these opportunities in term of four business capabilities: automation, engagement, insight/decision making and innovation. We discuss the differentiated effects that AI brings about and the implications for future IS research.
Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.
Recent studies on the construction and use of “small world representations” in strategic decision-making under Knightian uncertainty say little about how such representations should be updated over the implementation phase. This paper draws on the psychology of reasoning to take a step towards answering this question. We begin by theorizing small world representations and how the scenario spaces they contain are constructed and may be updated over time. We then introduce two well-known heuristic methods of inquiry, disconfirmation and counterfactual reasoning, translate them into practical procedures for updating scenario spaces, and compare the relative performance of these procedures in strategic situations of Knightian uncertainty. Our principal findings are that the procedure based on counterfactual reasoning is superior to the one based on disconfirmation with respect to (1) counteracting the confirmation bias, (2) promoting the exploration of the set of imaginable scenarios, and (3) facilitating action to mitigate or exploit the consequences of what would otherwise have been Black Swans. We close with some broader implications for the study of strategic decision-making under Knightian uncertainty.
Digitally induced complexity is all around us. Global digital infrastructure, social media, Internet of Things, robotic process automation, digital business platforms, algorithmic decision making, and other digitally enabled networks and ecosystems fuel complexity by fostering hyper-connections and mutual dependencies among human actors, organizations, structures, processes, and objects. Complexity affects human agencies and experiences in all dimensions including market and economic behaviors, political processes, entertainment, social interactions, etc. Individuals and organizations turn to digital technologies for exploiting emerging new opportunities in the digital world. They also turn to digitally-enabled solutions to cope with the wicked problems arising out of digitally-induced complexity. In the digital world, complexity and solutions based on digital technologies present new opportunities and challenges for information systems (IS) research. The purpose of this special issue is to foster the development of new IS theories on the causes, dynamics, and consequences of digitally-induced complexity in socio-technical systems. In this essay, we discuss the key concepts and methods of complexity science, and illustrate emerging new IS research challenges and opportunities in complex socio-technical systems. We also provide an overview of the five articles included in the special issue. These articles illustrate how IS researchers build on theories and methods from complexity science to study wicked problems arising out of digitally induced complexity. They also illustrate how IS researchers leverage the uniqueness of the IS context to generate new insights to contribute back to complexity science.
Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1–3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm’s forecast.
Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2643 .
This paper was accepted by Yuval Rottenstreich, judgment and decision making.
This paper reports on an exploratory, multisite case study of four organizations from the insurance, banking, telecommunications, and e-commerce industries that implemented big data analytics (BDA) technologies to provide individualized service to their customers. Grounded in our analysis of these four cases, a theoretical model is developed that explains how the flexible and reprogrammable nature of BDA technologies provides features of sourcing, storage, event recognition and prediction, behavior recognition and prediction, rule-based actions, and visualization that afford (1) service automation and (2) BDA-enabled human-material service practices. The model highlights how material agency (in the case of service automation) and the interplay of human and material agencies (in the case of human-material service practices) enable service individualization, as organizations draw on a service-dominant logic. The paper contributes to the literature on digitally enabled service innovation by highlighting how BDA technologies are generative digital technologies that provide a key organizational resource for service innovation. We discuss implications for research and practice.
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
Digital transformation, which often includes establishing big data analytics capabilities poses considerable challenges for traditional manufacturing organizations, such as car companies. Successfully introducing big data analytics requires substantial organizational transformation and new organizational structures and business processes. Based on the three-stage evolution of big data analytics capabilities at AUDI, we provide recommendations for how traditional manufacturing organizations can successfully introduce big data analytics and master the related organizational transformations.
Tony Lawson’s work on probability and uncertainty is both an important contribution to the heterodox canon as well as a notable early strand of his ongoing enquiry into the nature of social reality. In keeping with most mainstream and heterodox discussions of uncertainty in economics, however, Lawson focuses on situations in which the objects of uncertainty are imagined and can be stated in a way that, potentially at least, allows them to be the subject of probability judgments. This focus results in a relative neglect of the kind of uncertainties that flow from the existence of possibilities that do not even enter the imagination and which are therefore ruled out as the subject of probability judgments. This paper explores uncertainties of the latter kind, starting with and building on Donald Rumsfeld’s famous observations about known unknowns and unknown unknowns. Various connections are developed, first with Nassim Taleb’s Black Swan, and then with Lawson’s Keynes-inspired interpretation of uncertainty.
Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects.
The era of big data and analytics is upon us and is changing the world dramatically. The field of Information Systems should be at the forefront of understanding and interpreting the impact of both technologies and management so as to lead the efforts of business research in the big data era. We need to prepare ourselves and our students for this changing world of business. In this discussion, we focus on exploring the technical and managerial issues of business transformation resulting from the insightful adoption and innovative applications of data sciences in business. We end by providing an overview of the papers included in this special issue and outline future research directions.
Over the last three decades, the prevailing view of information technology strategy has been that it is a functional-level strategy that must be aligned with the firm's chosen business strategy. Even within this socalled alignment view, business strategy directed IT strategy. During the last decade, the business infrastructure has become digital with increased interconnections among products, processes, and services. Across many firms spanning different industries and sectors, digital technologies (viewed as combinations of information, computing, communication, and connectivity technologies) are fundamentally transforming business strategies, business processes, firm capabilities, products and services, and key interfirm relationships in extended business networks. Accordingly, we argue that the time is right to rethink the role of IT strategy, from that of a functional-level strategy-aligned but essentially always subordinate to business strategy-to one that reflects a fusion between IT strategy and business strategy. This fusion is herein termed digital business strategy. We identify four key themes to guide our thinking on digital business strategy and help provide a framework to define the next generation of insights. The four themes are (1) the scope of digital business strategy, (2) the scale of digital business strategy, (3) the speed of digital business strategy, and (4) the sources of business value creation and capture in digital business strategy. After elaborating on each of these four themes, we discuss the success metrics and potential performance implications from pursuing a digital business strategy. We also show how the papers in the special issue shed light on digital strategies and offer directions to advance insights and shape future research.
This study investigates how different types of “big data analytics” (BDA) usage influence organizational decision-making in the area of supply chain management (SCM). Drawing on decision-making theory and organizational information processing theory, we conceptualize two patterns of BDA usage for supply chain (SC) activities (BDA use for SC optimization and BDA use for SC learning) and report two complementary channels via which the two BDA usage patterns impact a supply chain organization’s BDA-enabled decision-making capability. An analysis of questionnaire data from supply chain managers representing 157 companies based in North American suggests that BDA use for SC optimization is directly associated with better decision-making capability. In contrast, the influence of BDA use for SC learning does not impact decision-making directly but indirectly, as its effect is fully mediated by organizational integration. We discuss the implications of these findings for future academic research and for managers in practice who seek to maximize business values from BDA implementations.
The introduction of machine learning (ML)in organizations comes with the claim that algorithms will produce insights superior to those of experts by discovering the “truth” from data. Such a claim gives rise to a tension between the need to produce knowledge independent of domain experts and the need to remain relevant to the domain the system serves. This two-year ethnographic study focuses on how developers managed this tension when building an ML system to support the process of hiring job candidates at a large international organization. Despite the initial goal of getting domain experts “out the loop,” we found that developers and experts arrived at a new hybrid practice that relied on a combination of ML and domain expertise. We explain this outcome as resulting from a process of mutual learning in which deep engagement with the technology triggered actors to reflect on how they produced knowledge. These reflections prompted the developers to iterate between excluding domain expertise from the ML system and including it. Contrary to common views that imply an opposition between ML and domain expertise, our study foregrounds their interdependence and as such shows the dialectic nature of developing ML. We discuss the theoretical implications of these findings for the literature on information technologies and knowledge work, information system development and implementation, and human–ML hybrids.
The data-centric revolution generally celebrates the proliferation of business analytics and AI in exploiting firm’s potential and success. However, there is a lack of research on how the unintended consequences of AI integrated business analytics (AI-BA) influence a firm’s overall competitive advantage. In this backdrop, this study aims to identify how factors, such as AI-BA opacity, suboptimal business decisions and perceived risk are responsible for a firm’s operational inefficiency and competitive disadvantage. Drawing on the resource-based view, dynamic capability view, and contingency theory, the proposed research model captures the components and effects of an AI-BA opacity on a firm’s risk environment and negative performance. The data were gathered from 355 operational, mid-level and senior managers from various service sectors across all different size organisations in India. The results indicated that lack of governance, poor data quality, and inefficient training of key employees led to an AI-BA opacity. It then triggers suboptimal business decisions and higher perceived risk resulting in operational inefficiency. The findings show that operational inefficiency significantly contributes to negative sales growth and employees’ dissatisfaction, which result in a competitive disadvantage for a firm. The findings also highlight the significant moderating effect of contingency plan in the nomological chain.
Researchers employ methodologies that rely on contemporary technologies to study the phenomenon. Recent advances in artificial intelligence (AI), particularly machine learning (ML), have intensified the speed, and our abilities, to create and deploy new knowledge for constructing theories (Abbasi et al. 2016). The availability of big data and ML tools is no longer the sole domain of academics. Business processes generate large amounts of data and now practitioners also deploy ML-enabled methodologies to create new knowledge through working theories.
The resource-based view (RBV) makes a significant contribution to strategy by explaining the relationship between resources and firm performance. Particularly in low uncertainty markets, executives have the foresight and time to build strategically valuable resources in current markets and leverage them into related markets. RBV is also relevant for understanding strategies for market entry, extending the value of technology resources, and broadening the locus of resources within ecosystems. Conversely, in high uncertainty markets like nascent or disrupted ones, RBV seems less germane. Resources may not yet exist or their value (and rarity) may be indeterminate (or changing). Here, we contribute the Strategy Creation (SC) view—joining strategizing by doing, thinking, and shaping. It offers a strategic logic that fits highly uncertain markets. Finally, we contribute the insight that RBV and SC are complements under moderate uncertainty, such as growth markets and technology transitions. Overall, we propose that uncertainty forms a crucial boundary condition for RBV that distinguishes between qualitatively different strategic logics.
With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations
in reallocating scarce resources to facilitate organizational learning in practice.
The emergence of big data has stimulated enormous investments into business analytics solutions, but large-scale and reliable empirical evidence about the business value of big data and analytics (BDA) remains scarce. This article presents the results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets. Using a unique panel data set that contains detailed information about BDA solutions owned by 814 companies during the time frame from 2008 to 2014, on the one hand, and their financial performance, on the other hand, we estimate the relationship between BDA assets and firm productivity and find that live BDA assets are associated with an average of 3–7 percent improvement in firm productivity. Yet we also find substantial differences in returns from BDA when we consider the industry in which a firm operates. While firms in information technology-intensive or highly competitive industries are clearly able to extract value from BDA assets, we did not detect measurable productivity improvement for firms outside these industry groups. Taken together, our findings provide robust empirical evidence for the business value of BDA, but also highlight important boundary conditions.
In making strategic decisions, managers implicitly or explicitly come to choose a cognitive strategy, by which we refer to the choices made as regards what type of information processing to engage in and rely on as the basis for a decision. Dual-processing theories of cognition recognize two types of information processing: non-conscious and conscious. There are two cognitive strategies—relying on intuition and engaging in analytic reasoning—that have a straightforward connection to the two types of information processing. However, managers often engage in reframing, that is, they deliberately attempt to rethink the background assumptions concerning how one approaches a decision-making situation. Despite the strategic importance of reframing, the foundations of this cognitive strategy remain theoretically underdeveloped. We argue that reframing involves both Type 1 and Type 2 processing in a complementary fashion. Specifically, reframing can be induced through conscious reflection and non-conscious processing during an incubation period. Furthermore, we argue that while reframing is a robust cognitive strategy across varying levels of environmental dynamism, dedicating time to reframing incurs significant opportunity costs, and can thus be employed only sparingly.
Despite the huge investment in data analytics tools, the necessary conditions required to obtain benefit from such investment deserves close investigation. In this study we utilize the knowledge-based view and data analytics competency literature to address two important research questions: (1) Does knowledge sharing have a mediating role on the impact of data analytics usage on the quality of firm decisions?; and (2) What is the role of data analytics competency in enhancing the quality of firm decisions through increasing knowledge sharing? Survey data collected from top and middle-level managers from 133 U.S.-based firms indicates that: the impact of data analytics use on the quality of firm decisions is fully mediated by knowledge sharing; the impact of knowledge sharing on firm decision quality is not significant and it is moderated by data analytics competency; and data analytics competency does not moderate the impact of data analytics use on knowledge sharing. Collectively, the findings provide a theory-based understanding of how data analytics use improves firm decision quality. The results also provide actionable guidelines for firms regarding the critical resources they need to invest in order to obtain benefits from using data analytics tools.
Despite significant academic and managerial interest in big data, there is a dearth of research on how big data impacts long‐term firm performance. Reasons for this gap include a lack of objective indices to measure big data availability and its impact, and the tendency of studies to ignore the costs associated with collecting and analyzing big data, assuming that big data automatically delivers benefits to firms. Focusing on how firms create and capture value from big data about customers, we use the resource‐based view (RBV) and three dimensions of big data (i.e., volume, variety and veracity) to understand when the benefits outweigh the costs. Relying on the number of downloads of mobile device applications, we find that volume of big data has a negative effect on firm performance. This result suggests that the ‘bigness’ of big data alone does not ensure value creation for a firm, and could even constitute a ‘dark side’ of big data. Because big data variety – measured as the number of types of information taken per each application – moderates the negative effects of big data volume, simultaneous high values of volume and variety allow firms to create value that positively affects their performance. In addition, high levels of veracity (i.e., a high percentage of employees devoted to big data analysis), are linked to firms benefiting from big data via value capture. These findings shed light on the circumstances in which big data can be beneficial for firms, contributing to a better theoretical understanding of the opportunities and challenges and providing useful indications to managers.
Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.
Understanding how organizations operate in different environments has been at the core of organizational research for decades. Three distinct bodies of literature have emerged, with limited cross-pollination among them: routines, heuristics, and improvisation. We add to the existing literature by studying these three types of organizational responses simultaneously via an in-depth longitudinal study of an organization that encountered increasing levels of environmental dynamism. We pay particular attention to explaining how and why routines broke down, prompting the emergence of heuristics or improvisations, as well as when these three responses were used in tandem and when they interacted with each other. Our theoretical model identifies the triggers of heuristics and improvisations and the focal context that led to routines breaking down. We define “focal context” as a constructed temporary reality that encompasses both the objective traits of the environment experienced by the organization at a particular point in time, as well as the subjective perceptions that organizational members had of that reality. We also identify the mechanisms of cognitive search and social convergence that led to the creation of nonroutine responses. Finally, we use our insights to clarify the existing overlaps in the conceptualization of the three organizational responses. Our field study is based on a mountaineering expedition to climb one of the most difficult sides of Mount Everest, the Kangshung face, an archetypical case that is particularly well suited to the development of a new theory in which rich data are required to study the phenomena.
Following Demetis & Lee (2016) who showed how systems theorizing can be conducted on the basis of a few systems principles, in this conceptual paper, we apply these principles to theorize about the systemic character of technology and investigate the role reversal in the relationship between humans and technology. By applying systems-theoretical requirements outlined by Demetis & Lee, we examine conditions for the systemic character of technology and, based on our theoretical discussion, we argue that humans can now be considered artifacts shaped and used by the (system of) technology rather than vice versa. We argue that the role reversal has considerable implications for the field of information systems that has thus far focused only on the use of the IT artifact by humans. We illustrate these ideas with empirical material from a well-known case from the financial markets: the collapse (“Flash Crash”) of the Dow Jones Industrial Average.
Whatever answer one gives to the question "Is information technology changing the world?," the answer contains reasoning about causality. Causal reasoning is central to IS theorizing. This paper focuses on the concept of causal structure, defined by Markus and Robey (1988) as a theorist's assumptions about causal influences in IS phenomena, and proposes a framework of causal structure, warranted by philosophy and social theory, specialized for the needs of Information Systems scholars. The three dimensions of the framework concern the theorist's views about the reality and meaning of causation (causal ontology), about that which is changed in causation (causal trajectory), and about the role of humans and/or technology in bringing about the change (causal autonomy). On each dimension, mutually exclusive alternative positions are described, backed by authoritative warrants, and illustrated with IS examples. The framework aims to offer useful insights into the nature of IS theory and the conundrum of theoretical contribution.
Despite the publicity regarding big data and analytics (BDA), the success rate of these projects and strategic value created from them are unclear. Most literature on BDA focuses on how it can be used to enhance tactical organizational capabilities, but very few studies examine its impact on organizational value. Further, we see limited framing of how BDA can create strategic value for the organization. After all, the ultimate success of any BDA project lies in realizing strategic business value, which gives firms a competitive advantage. In this study, we describe the value proposition of BDA by delineating its components. We offer a framing of BDA value by extending existing frameworks of information technology value, then illustrate the framework through BDA applications in practice. The framework is then discussed in terms of its ability to study constructs and relationships that focus on BDA value creation and realization. We also present a problem-oriented view of the framework—where problems in BDA components can give rise to targeted research questions and areas for future study. The framing in this study could help develop a significant research agenda for BDA that can better target research and practice based on effective use of data resources.
Securing financial resources from investors is a key challenge for many early-stage entrepreneurial ventures. Given the inherent uncertainty surrounding a decision to invest in these ventures, prior research has found that experienced investors rely heavily on their investor gut feel—that is, dynamic expertise-based emotion-cognitions specific to the entrepreneurship context. In this paper, I inductively find that rather than being based on rapid, nonconscious impulse, as much of prior literature would suggest, what investors call their “gut feel” is an elaborate “intuiting process.” This process serves a distinct purpose: it emboldens investors to make investments that would otherwise be considered overly risky and likely to lead to failure. In the theoretical model I present, I delineate how investors are guided by a predisposed stance on risk and uncertainty, which dictates the approach investors take toward managing the complexity of an investment opportunity—and how they cognitively and emotionally reframe investment risk into a compelling narrative that transcends avoidance behavior and leads investors to invest. These findings expand our overall understanding of the complex ways in which investors contend with the risks and uncertainties in entrepreneurial finance.
There is no theory in strategic management and other related fields for identifying decision problems that cannot be solved by organizations using rational analytical technologies of the type typically taught in MBA programs. Furthermore, some and perhaps many scholars in strategic management believe that the alternative of heuristics or “rules of thumb” is little more than crude guesses for decision making when compared to rational analytical technologies. This is reflected in a paucity of research in strategic management on heuristics. I propose a theory of “organizational intractability” based roughly on the metaphor provided by “computational intractability” in computer science. I demonstrate organizational intractability for a common model of the joint strategic planning and resource allocation decision problem. This raises the possibility that heuristics are necessary for deciding many important decisions that are intractable for organizations. This possibility parallels the extensive use of heuristics in artificial intelligence for computationally intractable problems, where heuristics are often the most powerful approach possible. Some important managerial heuristics are documented from both the finance and strategic management literatures. Based on all of this, I discuss some directions for theory of and research on organizational intractability and heuristics in strategic management.
In this essay, we revisit Ackoff’s (1967) classic “Management Misinformation Systems” and its five myths. The paper appeared at the dawn of the information systems (IS) field and shattered popular assumptions about designing and using IS. The paper shaped the direction and scope of scholarly discourse around information systems; in contrast to dominant claims at that time, he argued that managers swam in the abundance of irrelevant information, were victims of poor modeling and, consequently, poor understanding of their own decisions, participated in destructive communication due to conflicting goals, and had a poor understanding of how systems worked. Despite the passage of 50 years (and many revolutions in information technology), researchers in the IS field still regard Ackoff’s arguments as valid and rarely debate them. Yet, given the new information-rich environments and our nearly limitless capability to collect and analyze data, we may need to reexamine these arguments to correctly frame information systems’ contemporary effects on managerial decision making. We scrutinize Ackoff’s five assumptions in light of today’s IT and data-rich environments and identify key tenets that will reframe the disciplinary discourse concerning the effects of information systems. We identify significant shifts in research on decision making including the role of abduction, data layering and options, and intelligence augmentation. We honor the extraordinary legacy of Ackoff’s remarkable paper as an IS scholar by shaping the field’s future inquiries in the spirit of the original paper.
- This paper describes the process of inducting theory using case studies from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.
The publication of the Special Issue in Intensive Methods in MIS Quarterly may be seen as one critical event that has contributed to the normalization of qualitative research in the mainstream community of the discipline. This has happened in two ways: first, the special issue provided the avenue for publication of a critical mass of qualitative studies in MIS Quarterly, widely considered to be one of the two elite journals in the discipline at that time, thereby institutionalizing the acceptability and respectability of qualitative research at the highest level of published scholarship; and second, it inscribed exemplars and criteria of excellence, certified by the leading qualitative scholars, into the literature that became a shared resource and offered a set of norms for the entire IS community.
We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems (IS) community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.
Using a multidimensional framework of CEO temporal focus (the degree to which CEOs characteristically devote attention to perceptions of the past, present, and future), we propose that a company's rate of new product introduction (NPI) is predicted by a CEO's focus on each of the three distinct time frames in interaction with environmental dynamism. Based on a longitudinal (from 1996 to 2003) analysis of 221 firms in 19 industries, we show that, in stable environments, new products are introduced faster in firms headed by CEOs with high past focus, high present focus, and low future focus. In dynamic environments, new products are introduced faster in firms headed by CEOs with low past focus, high present focus, and high future focus. These findings demonstrate how CEO temporal attentional bias shapes the rate of NPI.