ABSTRACT To better manage costs and effectiveness of a team or enterprise, the organizational sciences, social sciences and medical field are seeking new theory to transform teams and enterprises with computational models of complex social behavior that create “smart” systems. We have proposed to fulfill these calls with new theory, but ours is still under development. In a field application, we studied an electronic Institutional Review Board (eIRB) operating across a large complex of military medical scientists and researchers in Department of Defense (DoD) hospitals and clinics. As part of a field study of how the eIRB has transformed processes for DoD, we completed three comparable focus groups at a small and three focus groups at a large research site, one focus group of the eIRB's system managers, and one focus group of a competing eIRB operating in DoD (the latter group results are not reviewed at this time). We found tentative support for our theory: more noise (entropy) is being generated at the small site along with less research performed; cultural noise at both sites reflected an intransigence by sites to be transformed by adopting standardized forms; and the meaning of the findings differed between the small and large sites.
Abstract Enterprise transformation has a serious impact on a firm’s survivability. Even though 70% of firms fail to transform (e.g., ToysRUs, Blockbuster, MySpace, Blackberry, etc.), not enough has been researched on factors that could improve transformation success. Our research explores four new factors—timing, leadership involvement, psychological ownership, and transparency—that influence transformation success. A survey was administered to a sample of 190 transformation experts that shared their transformation experiences at multiple firms. This is one of the few empirical studies that uncover key factors that significantly influenced transformation success at not just one firm but at multiple firms. In addition to contributing to theory, the findings provide practitioners data-driven knowledge that can be applied to their ongoing transformation efforts to increase their odds of beating the 70% failure rate.
Interdependencies amongst firms with complementary capabilities lead to the emergence of stable patterns of interfirm relationships observed in global value chains and ecosystems. But current standard industry classification systems group industries into higher order aggregates based on similarity criteria, ignoring the complementarities that induce interdependence. We show how systems theory can be used to design an industry classification system that captures the interindustry interdependencies manifested by buy-sell transactions between firms. Our arguments are three. First, that we can improve upon currently available industry classification systems by clearly identifying criteria for grouping industries into higher order aggregates such as sectors. Second, that a top level grouping based on demand will divide the economy into sectors in a manner that is consistent with global value chains and other configurations of interfirm networks. And third, that roles within demand-based sectors are the redundant feature of interindustry relations that allow us to describe the economy simply. We support our arguments with visualizations of over 53,000 of the largest interfirm transactions in the U.S. economy between 1976 and 2010.
Abstract Digital Transformation (DT) is an essential trend for manufacturing companies as digitalization of the value chain affects the entire company. Strategic management functions should consider DT technologies and their impact on assets and resources. The knowledge of the possible factors that influence DT positively may ease a realization of gains due to DT. In 20 qualitative interviews, we examined critical factors for DT's success in manufacturing companies. Based on the IS success model of DeLone and McLean, we have derived success factors that can trigger DT success. The success factors describe the three major dimensions in which DT takes place: technology, organization, and environment. The results show that DT can only be successful if companies collaborate with customers, suppliers and also other firms from the branch. A cultural change is necessary to enable an agile working environment as well as more interdisciplinary activities. It becomes evident that the choice of technology is essential. However, driving only technology forward is not enough to gain benefits from DT.
Systems engineering rigor has been used successfully in the aerospace and defense industries where the development cycles tend to be rather long. However, the practice of using a traditional waterfall, spiral or V-model systems engineering life-cycle framework, in other industries brings into question their appropriateness when considering the relative speed of new product development in industrial manufacturing. The purpose of this research is to investigate the applicability of incorporating systems engineering principles in the industrial sector to determine whether there is a statistical association with the overall growth of diversified industrial firms. This research focuses on investigating three systems engineering life-cycle approaches: incremental & iterative methods, lean enablers for systems engineering and agile systems engineering, using a semistructured interviewing approach with subject matter experts from the Fortune 500 diversified industrial sector. The research reveals that there are weak statistical associations between the use of the incremental & iterative and lean systems engineering life-cycle approaches when considering the financial growth of the diversified industrial sector. However, the research reveals that there is a strong statistical association between the financial growth of companies in the diversified industrial sector and the use of the agile systems engineering life-cycle approach.
The digital transformation is forcing organizations to change towards more data-driven business models. In this paper, we propose a conceptual framework that explains the role of innovation capabilities as a mediator between big data and business model. Using the design science research method approach, we built the framework based on the existing literature. We then applied the framework to the real-world context with three firms and refined it based on the feedback. This study contributes to big data research by pointing out the role of human and data-driven innovation capabilities in the big data value creation process. The developed framework is practitioner oriented, offering a systematic approach towards the development of big data capabilities.
This paper studies platform ecosystems, which refer to a novel solution built upon a platform coordinating business interactions between suppliers and users, particularly in the IT service sectors. Borrowing words from two fundamental firm theories—resource-based view (RBV) and transaction cost view (TCV)—we argue that the platform ecosystem works at the boundary between “market and hierarchy.” We also develop a stylized model for the platform ecosystem, in which attributes like installed-base and heterogeneity and platform’s investment are the primary ingredients for RBV and TCV, respectively. Our findings based on equilibrium analyses and simulations largely confirm that both RVB and TCV are valid for understanding a platform ecosystem. We, however, also identify some contingencies where RBV is limited, and propose a hypothesis that the platform’ investment is more crucial for fostering an ecosystem. This implies that the platform starts from the point near market and maneuvers the ecosystem toward a hierarchy, utilizing its investment as a driver.
The rise of digital platforms shows that they have the potential to transform existing industries. But do they also have the potential to create new markets where none currently exist? This article proposes that the worldwide secondary market in personal information could be dramatically transformed by the introduction of a Primary Personal Information Market (PPIM), facilitated by a digital platform. Drawing on design science methodology, market engineering theory, service innovation and digital platform theories, we present a critical Service Architecture for digital platforms to ensure scalability and potential commercialization. The platform is designed to collect and monetize personal information on behalf of the original creators of that information without compromising privacy or security. We present the design evolution of a PPIM digital platform culminating in a Service Architecture Framework, based on academic experts’ iterative feedback in stages over three years. A prototype PPIM is described and its viability from the perspective of the digital platform’s four categories of market participants is evaluated. This article addresses a knowledge gap in the literature by presenting a novel illustration of how digital platforms would transform industries and their adoption would affect individual data contributors’ value creation and capture.
As a radical and risky change approach, business transformation enables organizations to add substantial value and help overcome major environmental pressures. The academic and practice literature suggests business transformation frameworks to guide organizations through such a journey. This article presents a comparative analysis of three business transformation frameworks from the literature. It concludes that business transformation frameworks are complementary and their use depends on the organizational context. This analysis is then leveraged to develop an adapted framework to a specific industrial situation. The article outlines the characteristics of this specific industrial context and how it influences the adaptation of a business transformation framework. An overview of the adapted framework is presented.
This article addresses the prospects for automating intelligence versus augmenting human intelligence. The evolution of artificial intelligence (AI) is summarized, including contemporary AI and the new capabilities now possible. Functional requirements to augment human intelligence are outlined. An overall architecture is presented for providing this functionality, including how it will make deep learning explainable to decision makers. Three case studies are addressed, including driverless cars, medical diagnosis, and insurance underwriting. Paths to transformation in these domains are discussed. Prospects for innovation are considered in terms of what we can now do, what we surely will be able to do soon, and what we are unlikely to ever be able to do.
Enterprise architecture (EA) is more of an applied discipline than a theoretical framework. This is mainly because it is expected to guide an organization’s practices, such as technology infrastructure management, business process design and information governance, toward sustainability and competitiveness. However, this has not often been the case in many organizations, in that the concept continues to gain more theoretical attention than implementation and practice. Thus, the objective of this study was to examine and gain an understanding of the implications, theorizing of EA has on both academic and business industries. The interpretivist philosophy, qualitative methods, and case study approach were employed in the study. From the analysis of the qualitative data, the implications of theorizing EA were identified. Based on the findings, a model was developed. The model draws on and shows the interconnectivity and interrelationship between EA theory and practice.
ABSTRACT Mass production firms in high labor cost environments need to develop a capability of continuous innovation to survive in the global marketplace. This need is complicated by the “productivity dilemma”; that a focus on efficiency inhibits innovative activity. Operations management literature suggests that resolution of this dilemma is achieved through “organizational ambidexterity,” balancing improvement (exploitation) and innovation (exploration). Building upon current theory, this article uses case study research to illustrate how improvement and innovation capabilities evolved, and how ambidexterity was achieved in an auto parts manufacturing organization, as it implemented novel assembly processes over a 4-year period. Results show how capabilities improve by altering underlying routines as a result of experiential learning, and that ambidexterity is achieved as a temporal sequencing among different levels within the organization. Developing innovation capability in a mass production organization requires organizational alignment between the Management, Engineering, and Production groups, as each has a specific role in the process.
The growing complexity in the technological products and systems that we design, manage and operate may limit the future potential for component and architecture innovation. In the meantime, systems-of-systems are emerging. System-of-systems innovation may bypass the complexity within an existing system, and synthesize it with other systems and technologies to improve their utilities or derive novel functionalities from the new whole. Despite the increasing attention to systems-of-systems as ex post phenomena, the proactive pursuits for system-of-systems innovation opportunities remain unguided. In this article, we first elucidate the theoretical foundations, including expansionism, synthesis and complementarity, of system-of-systems innovations with various examples. We propose a methodology of using a technology map to aid in the search for technologies and the conception of new system-of-systems concepts. Further, we also discuss and prescribe a few actionable approaches and strategies to conquer the challenges in implementing new system of systems concepts and capture the potential values created from system-of-systems innovations.
Gaining an “information edge” over the competition can be a powerful catalyst for enterprise transformation. However, projects for fielding enterprise information systems too often fail, especially in the government. Perhaps then, enterprise information systems project managers as a community do not manage risk effectively enough. Here, we hypothesize a transformation approach to risk management, based on risk models from the financial sector, wherein the Moore's Law exponential growth curve serves as a baseline metaphorically equivalent to the (financial) market. Moore's Law is a recent formulation of the long-observed exponentially decreasing cost per unit of technological functionality. This exponential law is a self-sustaining artifact of competitive pressure. Success in the market requires continually evolving offerings in step or ahead of customer expectations. Enterprises that achieve this success do so by harvesting the plug-and-play efficiencies of open system approaches.
In this study, we compare the direct outsourcing strategy with the indirect outsourcing strategy, whereby intermediaries are employed to carry out activities related to sourcing from suppliers. Specifically, we develop a theoretical framework and a set of propositions that focus on the impact of outsourcing knowledge, coordination capability, and organizational complexity on a firm's strategy regarding the employment of an intermediary during the determination of a manufacturer for its new product. Although direct outsourcing can provide some advantages over traditional indirect outsourcing, it is characterized by high levels of organizational complexity and may introduce new sources of supply risk. We discuss situations in which the direct outsourcing of new products can be advantageous as well as risk mitigating.
This article provides a case study of Deere & Company's enterprise transformation since 2000. In order to transform, Deere redesigned its core processes and optimized its resources to improve the value to its key constituencies. The first phase of the transformation focused on resource efficiency to create investor value. The second phase focused on global growth through creating customer value. The articles answers four questions. First, what drove Deere to transform? Second, what were the major elements of Deere's transformation? Third, has Deere's transformation been successful? Fourth, what was the applicability of Rouse's theory of enterprise transformation to Deere's transformation? The study is based on public sources. Those include annual reports, investor presentations, earnings call transcripts, news articles, market share research, and extensive Deere-centric business literature. Qualitative research methods include pattern-matching, explanation-building, time-series analysis, and logic models. Quantitative research methods include financial accounting and linear regression.
Innovation and technological change have been at the center of our society for most of the past 50 years. During that time, academics have tried with varying success to study the processes that drive growth in innovation and technology. This paper provides a historical perspective on the trajectory of research on innovation and technological change. Based on our review, we offer three positions that run counter to the status-quo. First, that a model of innovation should explain the dynamics of market competition rather than to simply define the source of economic rents. Second, that the manner in which we should study innovation and technological change is much more behavioral in nature. Third, we need to unpack the organization to make progress in understanding innovation. With this in mind, we develop a taxonomy of the relevant literature in strategy, economics, product development, and technology management. We then propose some principles for modeling innovation and technological change in a manner that brings together these fields of study.
Rarely can the output of a cross-functional team be accurately decomposed into specific functional contributions. Research in organizational theory has placed attention on the challenges associated with incentive provision for such teams. Yet, most of this work makes a critical but overlooked assumption that value can be verifiably decomposed into its constituent parts. We consider a team project where a portion of the output cannot be verifiably attributed to each function. Our results indicate when an organization should offer incentives based on the total output (team-based) as opposed to incentives based on a verifiable fraction of the total value. When there exists sufficient project uncertainty, the organization can expect to generate greater value through the use of incentives based on the teams combined value. Additionally, when the interdependence between the functions has a positive impact on value creation, the organization benefits more from the provision of incentives based on the verifiable portion of a function’s contribution to the project. We also show how greater disparity between the cost of effort the functions incur can prompt the organization to decouple the team members’ efforts and use incentives based only on the verifiable component of a function’s input.
One global movement to increase organizational efficiency is the “lean” movement and while lean implementations in organizations have had positive impact, there remain concerns about the rate and sustainability of successful implementation. The current article discusses the use of collective system design as a way to deal with these concerns for lean initiatives and other systematic changes in organizations. Collective system design provides a language for describing a system design with the goals of a system stated as functional requirements met by particular means, called physical solutions that are proposed as a hypothesis to achieve functional requirements. The current article describes the collective system design methodology and provides a manufacturing system re-design case evaluation that examines the synergy with value stream mapping. Leadership implications are then examined based on the case-study research.
The U.S. healthcare system is arguably undergoing its most significant transformation since the introduction of Medicare and Medicaid in the 1960s. As then, covering more citizens is a motivation, but the larger effort now is toward creating a high-quality, safe, precise, and cost-effective system. There is broad agreement that accomplishing these objectives requires widely adopted, interoperable digital health records and related clinical tools, including those used by patients. In parallel, sophisticated analysis of the data from these systems and tools is required to gain deeper understanding of care processes and optimal care delivery. Current electronic health record systems and tools are in need of innovative approaches to increase their usability and impact on care. Health Level Seven's (R) rapidly emerging Fast Healthcare Interoperability Resources standard may provide a practical solution to interoperability and for innovation to feedback new knowledge gained from past care to support better future decisions by physicians and patients.
