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This paper seeks to establish a design for cross‐organizational workflow based on logical channels of communication. A set of scenarios is established which can be used to test the effectiveness of future architectures.

Starting with scenarios based on cross‐organizational business transactions, designs a set of sequence diagrams, analyzes these diagrams, and then deduces the need for certain system capabilities.

Current approaches to web services focus on just one channel – that of invocation. In order to handle the full gamut of cross‐organizational workflow, channels devoted to flow, monitoring, negotiation, and interpersonal interaction are also necessary.

Extensions to this research might include designs for integrating these multiple channels. Such designs can be tested against the scenarios discussed in the paper. In particular, research related to the semantic web might extend the ideas raised here.

Companies implementing web services may want to augment the current technology with enhancement of their own in order to avoid inadvertently reducing their channels of communication with trading partners. A properly constructed monitoring channel has the potential to change the nature of complex workflow.

Those designing and implementing web service technology may find value in the articulation of a set of complex scenarios which can be used to test alternative designs and implementations. The discussion of cross‐organizational monitoring is new, and has broad implications for business.

An important question facing business scholars is whether and how organizations may best adapt their investments, resource profiles, and strategies to the demands of their particular environments. While a broad literature describes organizational design principles that may assist in this regard, more recent work builds on Kauffman's (1993) NK model of biological evolution to explore how selection mechanisms and adaptive principles promote firms' exploitation and exploration efforts. This research stream has made contributions regarding the importance and efficacy of various internal adaptive factors in particular environmental settings. For instance, Levinthal (1997) shows that, despite extensive adaptation efforts, the influence of imprinting persists in complex environments with many local peaks. Rivkin (2000) demonstrates that NK complexity degrades the efficacy of search, compelling imitators to rely on search heuristics rather than adaptation via local learning. Rivkin and Siggelkow (2003) explore the tradeoffs between exploration and stability, and describe how particular organizational attributes, such as vertical hierarchy and group- or firm-level incentive systems, influence the flow of information throughout the organization. These as well as other contributions have added precision to the conceptualization of environments and sharpened understanding of organization by describing precisely how interdependencies across investment choices and/or resource profiles affect adaptation efforts.

This chapter focuses on how corporate entrepreneurs seize opportunities and deal with threats through resource acquisition, control, and use. When corporate entrepreneurs fail to gain control of preferred resources they must rely on their ability to optimize their use of resources on hand in order to avoid the typical limitations inherent in a constrained set of resources. However, control of resources, whether existing or supplementary, by itself is an insufficient basis for influencing performance. Performance also depends on an organization’s capacity to deploy resources in combination with strategically important organizational processes to affect a desired end. The way in which corporate entrepreneurs utilize their resources is likely to have a more significant effect on performance than is merely having control of them. The current research aims to elaborate on how corporate entrepreneurs can become more resourceful by using a vacillation approach to resource acquisition and utilization. In this context, vacillation is movement between exploration and exploitation, or knowledge acquisition and knowledge integration from a knowledge management perspective. Vacillation is distinguished from the “balance” hypothesis prevalent in the organizational ambidexterity literature. A balance hypothesis states that both exploration and exploitation may be pursued simultaneously either by creating structural or contextual organizational ambidexterity. Here, we explain how vacillation enables an organization’s corporate entrepreneurship posture to lead to improved performance. In this chapter, we first describe the extant literature and construct relationships between corporate entrepreneurship posture, organizational resource level, vacillation, and organizational performance. We then analyze the learning processes associated with vacillation and discuss the research and managerial implications associated with the proposed relationships.

To assess the impact of TCE on the field of strategy, we first quantified the distribution of TCE-related research articles across all disciplines and fields. Specifically, we identified every article that appeared in a journal included in the Institute for Scientific Information's (ISI's) Web of Knowledge between 1975 and 2008 and that included among its keywords some variation of “transaction costs.” We then removed those articles for which this term clearly did not refer to transaction costs of the Coasean kind (primarily articles in finance and computing, for which “transaction cost” has a different meaning). Finally, we categorized each journal according to its discipline or field. Granted, this requires some judgment, but we attempted to be objective in our categorizations.¹ As Table 1 shows, articles that are self-described as part of the TCE research stream have appeared more frequently in strategy journals than in the journals of any other discipline or field. We interpret this as evidence of TCE's impact on strategy, and of the importance of the strategy field to TCE.

The purpose of this paper is to identify and describe the reference works useful for finding written information on the North American Indian (that is, Indians presently and in the past living in what is now the United States and Canada).

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

The primary objective of this research is to investigate the impact of task complexity on users’ performance in the context of using computer‐based technologies with auditing tasks. A laboratory experiment was performed with 121 undergraduate auditing students. The task complexity is defined in form of structured and unstructured tasks. The dependent variables were accuracy in solving problem and certainty of the correctness of solution. The results suggested that the effect of task complexity on accuracy in solving problems was significant for structured tasks. In addition, the significant effect was also found on certainty of the correctness of solution for structured tasks. The findings suggest the strategy for improving user performance in solving problem and certainty of the correctness of solutions toward a given task by giving users a structured task to perform. In addition, the findings also suggest the need for research on how to benefit from using computer‐based technologies in the context of solving structured task.

The purpose of this study is to examine big data capability's impact on product improvement and explore supply chain dynamics including relationship building and knowledge sharing as important contribution to big data capability.

The research model is tested with survey data. Data analysis results empirically support the proposed model and the hypothesized relationships between the concepts.

First, the hypothesis testing results of this study show that big data capability directly enhances product improvement. Second, this study shows that supply chain relationship building and knowledge sharing are positively related to the development of big data capability.

In supply chain management, there are multiple factors, besides relationship building, that serve as conditioners to knowledge sharing's effect on product performance. We only examined the role of relationship building in this area.

Findings from this research encourage firms to take advantage of their supply chain resources to develop a big data capability that positively contributes to firm performance.

The contribution lies in that it brings to light this step that connects big data capabilities and market and financial performance, which is missing in prior research. This study contributes to the literature by identifying supply chain management activities, more specifically, supply chain relationship building and knowledge sharing, as antecedents to big data capability. This helps to extend this emergent enterprise of big data research to a new area and points to new directions for future research.