This study develops and validates an instrument for assessing organization improvisational capability (OIC) for potential use in future empirical studies. A definition of OIC and its dimensions are proposed based on an intensive literature review. An initial three-dimension, nine-item OIC scale derived from the literature is validated iteratively and then refined through a rigorous process into a two-factor, eight-item scale. The final scale demonstrates adequate psychometric properties, including reliability and convergent and discriminant validity. As such, this study contributes to a deeper theoretical understanding of the OIC construct and the appropriate application of such scales in future empirical studies. Future research on organization performance, agility, and strategy could utilize the proposed scale to study how OIC creates business value.

Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource-based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy-set qualitative comparative analysis to analyse multi-source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations.

This paper identifies factors that motivate students to pursue a vendor-endorsed ERP award by integrating concepts from motivation theory and constructs from technology acceptance literature. We developed a web-based survey with closedand open-ended questions to collect both quantitative and qualitative data, respectively. Students in information systems courses were solicited to participate in the survey. We collected data from 2010 to 2014. Our analysis shows that Perceived Value and Social Influence are significant predictors of students’ intentions to pursue a vendor-endorsed ERP award.

A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.

Flipped learning approach becomes popular in higher education. Many studies have focused on discovering benefits of the flipped learning but fell short on demonstrating the design of flipped learning. Computer programming classes are perfect match for the flipped learning approach. This paper presents a series of detailed flipped learning activities of the nested if statement for an introduction to Java class to first demonstrate the structure of a flipped classroom and to lay the foundation of future research.

Event-driven architecture is one of IT architectures introduced to assist enterprises operate in a real-time environment such as healthcare. As IT business value generation is a complex process, we explore configurations of event-driven architecture and other organizational elements in achieving healthcare performance. We purpose an alternative non-regression-based way, configurational approach, to study such process. Drawing on the configuration view, we attempt to capture the complexity of interactions among EDA-enabled capability and organizational elements needed to achieve higher health care quality. We tested our model with both primary and secondary data. Results show three different configurations with high levels of responding capability, dynamic capability and physicians' resistance to IT changes present in all three paths. Our findings advance understanding of a complex business value generation process and also provide practical guidance for healthcare managerial practices.

To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.

Technology commercialization (TC) contributes to maintaining the competitive advantage of high-tech firms, but although researchers have established that product innovation and new product development are enhanced by cross-functional collaboration and organizational knowledge activities, this may not be the case for TC. Drawing on the knowledge-based view and the influence of cross-functional collaboration, the main goal of this study is to unravel the relationships among cross-functional collaboration, knowledge creation and TC performance in the high-tech industry context. Empirical findings from our survey of 203 marketing and R&D managers and employees in Taiwanese high-tech companies indicate that cross-function collaboration reveals fresh opportunities for creating knowledge and commercializing technologies. Our results also suggest that knowledge creation plays an important role in TC performance by partially mediating the relationship between cross-functional collaboration and TC performance. The contributions of this study provide new insights into industrial marketing literature by proposing a cross-functional collaboration-enabled TC model that takes into account the effect of knowledge creation.

Purpose – Enterprise architecture (EA) aligns information systems with business processes to enable firms to reach their strategic objectives and, when effectively employed by organizations, can lead to enhanced levels of performance. However, while many firms may adopt EA, it is often not used extensively. The purpose of this paper is to examine how performance expectancy (PE) and training affect the degree to which organizations use EA. Design/methodology/approach – The paper employed a survey method to gather data from IT professionals, senior managers, and consultants who work within organizations that have adopted EA. Covariance-based structural equation modeling was used to analyze the research model and test the hypotheses. Findings – The paper found PE to be a significant predictor of EA use. In addition, training is also shown to enhance use of EA while also playing a mediating role within the relationship between PE and use of EA. Research limitations/implications – The study is limited by the focus only on training as an intervention. Other mediators and/or moderators such as top management support and organization culture may also play an important role and should be examined in future studies. Nonetheless, the study demonstrates the critical role that training can play in facilitating widespread use of EA within organizations. Practical implications – Widespread use is a critical success factor for organizations that want to gain the maximum possible benefit from EA. To achieve extensive use, the study suggests that organizations that adopt EA should consider implementing a formal and robust education and training program. Originality/value – This study extends the research on information technology training by examining the role of training as an intervention within the technology acceptance paradigm. The paper also contributes to the literature regarding post-adoption innovation diffusion by demonstrating the efficacy of organizational training in promoting widespread usage.

Big data are challenging organizations to find a thoughtful, holistic approach to data, analysis and information management to facilitate timely and sound decisions making, and in turn to gain competitive advantages. Managing big data is not a simple technical issue, but a complex managerial and strategic one. To achieve the vast potential of big data not only will enterprise IT architectures need to change, firms also need a new strategy, a new mind set, and a capability to deal with unexpected environmental turbulences. In this paper, we present a conceptual model and a novel analysis method, fuzzy set Qualitative Comparative Analysis to model and interpret interdependent non-linear relationships among elements and the outcome, performance. We posit that data management strategy, big data competence, IT capability and organization improvisational capability are interdependent and mutual reinforcing that form a network of nonlinear influential factors for firm decision quality and in turn, performance.

To date, the health care industry has paid little attention to the potential benefits to be gained from big data. While most pioneering big data studies have adopted technological perspectives, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the development, architecture and component functionalities of big data, and identifies its capabilities, including traceability, the analysis of unstructured data and patterns of care, and its predictive capacity to support healthcare managers seeking to formulate more effective big-data-based strategies. Our findings will help healthcare organizations respond strategically to the challenges they face in today's highly competitive healthcare market.

In this study, we examine the influence of a firm's environmental factors on its intention to adopt software as a service (SaaS). We operationalized our assessment of a firm's environmental pressures as mimetic, coercive and normative pressures and examined the moderating role of perceived technology complexity. Mimetic forces are pressures to copy or emulate other organizations’ activities, systems or structures. Coercive pressures are formal or informal pressures exerted on organizations by other organizations upon which they are dependent. Normative forces describe the effect of professional standards and the influence of professional communities on an organization. We empirically tested our research model using data from 289 valid survey responses. The results provide support for the assertion that there are both significant direct and interaction effects that influence a firm's SaaS adoption intention. Most important was the significant interaction effects between mimetic pressure and perceived technology complexity. This suggests that the complex relationships proposed by institutional theory and diffusion of innovation help to describe how environmental pressures and perceived technology complexity combine to affect intention to adopt an emerging technology. The theoretical contributions of this study are (i) we integrated, tested and validated mature theories in today's supply chain era with a new but rapidly diffusing technology, (ii) and we answered the call to include practical technology artifacts in information systems studies. From a practical perspective, through this work managers may develop a better understanding regarding environmental factors and whether or not they should consider these issues for their firm when formulating an intention to adopt SaaS.

Purpose– Enterprise architecture (EA) aligns information systems with business processes to enable firms to reach their strategic objectives and, when effectively employed by organizations, can lead to enhanced levels of performance. However, while many firms may adopt EA, it is often not used extensively. The purpose of this paper is to examine how performance expectancy (PE) and training affect the degree to which organizations use EA.Design/methodology/approach– The paper employed a survey method to gather data from IT professionals, senior managers, and consultants who work within organizations that have adopted EA. Covariance-based structural equation modeling was used to analyze the research model and test the hypotheses.Findings– The paper found PE to be a significant predictor of EA use. In addition, training is also shown to enhance use of EA while also playing a mediating role within the relationship between PE and use of EA.Research limitations/implications– The study is limited by the focus only on training as an intervention. Other mediators and/or moderators such as top management support and organization culture may also play an important role and should be examined in future studies. Nonetheless, the study demonstrates the critical role that training can play in facilitating widespread use of EA within organizations.Practical implications– Widespread use is a critical success factor for organizations that want to gain the maximum possible benefit from EA. To achieve extensive use, the study suggests that organizations that adopt EA should consider implementing a formal and robust education and training program.Originality/value– This study extends the research on information technology training by examining the role of training as an intervention within the technology acceptance paradigm. The paper also contributes to the literature regarding post-adoption innovation diffusion by demonstrating the efficacy of organizational training in promoting widespread usage.

Two research gaps were identified in technology innovation adoption, namely the rarity of innovation adoption theory on organizational and environmental factors. To fill these gaps, we decided to focus our review on the external environmental factors of organizational innovation adoption from January 2000 to May 2014. Major theories are presented. An integrated conceptual model is proposed. This review and the proposed conceptual model offer researchers a starting point to examine the external environment effects on a firm’s innovation adoption decision. In different stages, firms should use different strategy, focus on main issue of that stage, and use different resources to deal with the environmental pressures.

In our previous study, we put forth a working agent based model to solve social problems such as organizational decision-making. We presented a classical uncertainty model. For organizational tradeoffs, the uncertainty principle means that under interdependence, the probability of applying sufficient attention to a plan or to execute it shifts uncertainty in an opposing direction, and vice versa, iff the state of interdependence continues (Note: the symbol iff means “if and only if”). As time passed, we witness a new econophysics (e.g., McKelvey, Salmador, Morcillo, & Rodriguez-Anton, 2013) model of the interaction. Unlike the classical model proposed by Conant and Ashby (1970), econophysics indicates that adaptability formed by cooperation and knowledge is the key to team and organizational success. However, these aforementioned models are passive to interdependence. Scholars have proposed that social systems operate in states of interdependence (Smith & Tushman, 2005), but interdependence causes uncertainty (Lawless, 2013), but the mathematics becomes intractable (Jamshidi, 2009). Schweitzer, Fagiolo, Sornette, Vega-Redondo, Vespignani and White (2009) claim that the effort to mathematically model and control social interdependence has not been successful. We are developing a mathematical model of social uncertainty relations to replace traditional models of the interaction, as well as an update of our previous model.1 With our mathematical model of interdependence, we propose that indirect control becomes possible. BACKGROUND

Healthcare organizations are looking for opportunities to create new business model and value that can be implemented through information technology (IT) enabled transformation. Big data, an overwhelming phenomenon which has been addressed through various new and old data management technologies, hold the key to healthcare transformation. To address this, we developed a big-data-enabled transformation model based on practice-based view showing that the relationships among big data capability, big-data-enabled transformation practice, benefit dimensions, and firm performance. We tested this model by analyzing secondary data regarding big data in the healthcare context. Our results not only conceptually defined four big data capabilities but also found two significant path-to-performance chains. The contributions of this study are twofold. For management research, we establish a big-data-enabled transformation model to explain how big data leads to firm performance. For practitioners, we identify potential patterns that will help understanding big data's potentials and capabilities.

Conceptual database design is a difficult task for novice database designers, such as students, and is also therefore particularly challenging for database educators to teach. In the teaching of database design, two general approaches are frequently emphasized: top-down and bottom-up. In this paper, we present an empirical comparison of students’ performance between these two approaches in a conceptual data modeling exercise. Our results indicate that, while prior database education had a significant effect on the quality of design performance, the chosen approach did not. The findings suggest that database educators should integrate both top-down and bottom-up approaches in database design showing the differences and similarities between the two approaches to improve students’ learning of data modeling.