The Benefits of Enterprise Architecture in Organizational Transformation

Today, as organizations constantly adjust their activities to meet ever-changing circumstances, continuous business transformation is taking place. However, planning and steering this transformation can be a daunting task as complexity has been built into the organization over the years. Enterprise Architecture (EA) has been widely adapted as a planning and governance approach to manage the complexity and constant change, and to align the organization toward a common goal. This article studies the EA benefit-realization process by clarifying how EA benefits are realized. Specifically, the focus is on the strategies, resources, and practices which the EA benefits stem from. The findings, derived from an in-depth case study, show that the EA benefit-realization process con- stitutes a long, intertwined chain of activities. Organiza- tions benefit from EA through various means: from the initiation, when comprehensive understanding starts to form, until years later, when measurable outcomes such as cost savings materialize. Suggestions on what to incorpo- rate into EA programs are presented

Measuring Enterprise Architecture success: a tentative model for measuring success

Interest towards Enterprise Architecture (EA) has been increasing during the last few years. EA has been found to be a crucial aspect of business survival, and thus the importance of EA implementation success is also crucial. Current literature does not have a tool to be used to measure the success of EA implementation. In this paper, a tentative model for measuring success is presented and empirically validated in EA context. Results show that the success of EA implementation can be measured indirectly by measuring the achievement of the objectives set for the implementation. Results also imply that achieving individual's objectives do not necessarily mean that organisation's objectives are achieved. The presented Success Measurement Model can be used as basis for developing measurement metrics

The Study of Crowdsourcing in Knowledge Management: The Role of Employees\u27 Innovative Behavior on Job Performance

Employees are a main source of innovative ideas via their insights on companies’ products, processes, customers, and competitors. Enterprise crowdsourcing systems (ECSs) are used to collect, refine, and realize ideas. However, only a small percentage of employees submit ideas – about 7.7% at Pfizer, 2% at HCL Technologies, and 3% at Polaris Industries. Why is employee participation low? More specifically, what factors can lead employees to actively use ECS to submit and share their innovative ideas for improving their job performance? In this research, we used a multi-actor dyadic survey to survey 183 employees and their managers and conducted data analysis to understand the impact of ECS factors on employees’ job performance. Partial Least Squares (PLS) approach using Smart PLS was used to test both the measurement and structural models, and the results lend support for the proposed research model. The findings of the study confirm that knowledge sharing and employees’ cognitive features have a positive effect on effective knowledge application (EKA), and in turn, EKA increases employees’ ECS satisfaction, innovative behavior, and job performance. The study also confirmed that employees’ ECS satisfaction and innovative behavior have a positive effect on their job performance. The findings of this study can help organizations refine their ECSs and innovation initiatives to increase employees’ participation, innovative behavior, and job performance by enabling and supporting knowledge sharing among them, and implementing ECS with a solid, reward system meeting employees’ intrinsic and extrinsic motivational factors

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Initiating Factors Affecting Information Systems Project Success

Information systems (IS) projects are complex and costly, but only a 3rd of IS projects are successful; the Standish Group reported that 32% of IS projects were successful in 2012. Although investments in research have led to improvements in practice, there is a general perception that management failures are responsible for the low rate of IS project success. The effects of initiating factors on project outcome had not been sufficiently explored; few IS researchers have explored the initiation phase. The purpose of this grounded theory study was to explore project initiation factors, including relational, as well as decision-making aspects, and how they might be addressed to enhance the possibility of success. The research questions were oriented at identifying key initiation factors, how they might be managed to promote project success, and how decision-making factors at initiation might facilitate project success. A conceptual framework consisting of chaos theory and Ashby\u27s law of requisite variety was used. Purposive and snowballing sampling techniques were used, and 24 IS managers and project managers were interviewed. A 3-stage data analysis approach was used and included open coding, focused coding, and theoretical coding. Key themes identified included project governance and management, as well as stakeholder engagement. The emergent theory of IS project initiation indicated that the factors represented by the themes must be identified during initiation but implemented throughout the project lifecycle to ensure project success. Positive social change may be realized as IS managers, and project managers apply the findings and recommendations to achieve project success and avoid costly failures thus benefiting both companies and customers

ENTERPRISE ARCHITECTURE EVALUATION: A SYSTEMATIC LITERATURE REVIEW

By being holistically preoccupied with coherency among organizational elements such as organizational strategy, business needs and the IT functions role in supporting the business, enterprise architecture (EA) has grown to become a core competitive advantage. Though EA is a maturing research area, little has been done to understand how e.g. projects, application or other organizational elements contribute to the overall EA. The current paper presents a literature review on EA evaluation. Different types of evaluation are a necessity in order to ensure that EA demands are being met by disparate IT initiatives. Still, EA evaluation has attracted little attention within academic literature. Thus, the aim of the current review is to get an overview of the topic, which can serve as a foundation for further development of the field. Overall, the study shows that while little research has been done within this area, research is especially lacking regarding empirical studies of how EA evaluation unfolds in practice, while holistic views on EA evaluation is almost non-existin

Exploring ERP-enabled Technology Adoption: A Real Options Perspective

For decades, practitioners and scholars have focused on achieving optimal values in and benefits from enterprise resource planning (ERP) systems. Given that scholars have identified ERP systems as having option-like characteristics such as the capacity to create an information technology (IT) platform that enables the adoption of subsequent function-specific applications, we face a need to explore the linkage between post-ERP systems implementation and subsequent ERP-enabled technology adoption. We used real options theory to explore the underlying relationship between the initial ERP system implementation and subsequent technology adoptions. We surveyed 519 IT executives in the United States and found that the level of technology uncertainty, managerial flexibility, and formal real option analysis in ERP adoption decisions influenced the organizational relative advantage of subsequent non-ERP technologies. Our results also reveal that the level of uncertainty had a negative relationship with ERP-enabled technology adoption, while formal real option analysis in ERP adoption decisions positively influenced ERP-enabled adoption