Organizational Readiness for Business Intelligence and Analytics Systems Success

BI&A systems have the potential to improve business performance by facilitating innovations, creating new products and service, and enhancing decision making effectiveness. However, it requires certain technological and organizational capabilities to fully realize the values of BI&A systems. This study investigates how an organization needs to prepare itself to harvest from its investments in BI&A systems. We build a model using the contingency approach to test factors that affect the success of BI&A systems. The insights from this study can inform managers of business organizations about their organizational readiness for the success of their BI&A systems and identify best practices to implement BI&A systems in business organizations. It will also help advance our knowledge in how to accurately assess the success of BI&A systems

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

A Proposal for Supply Chain Management Research That Matters: Sixteen High Priority Research Projects for the Future

On May 4th, 2016 in Milton, Ontario, the World Class Supply Chain 2016 Summit was held in partnership between CN Rail and Wilfrid Laurier University’s Lazaridis School of Business & Economics to realize an ambitious goal: raise knowledge of contemporary supply chain management (SCM) issues through genuine peer-­‐to-­‐peer dialogue among practitioners and scholars. A principal element of that knowledge is an answer to the question: to gain valid and reliable insights for attaining SCM excellence, what issues must be researched further? This White Paper—which is the second of the summit’s two White Papers—addresses the question by proposing a research agenda comprising 16 research projects. This research agenda covers the following: The current state of research knowledge on issues that are of the highest priority to today’s SCM professionals Important gaps in current research knowledge and, consequently, the major questions that should be answered in sixteen future research projects aimed at addressing those gaps Ways in which the research projects can be incorporated into student training and be supported by Canada’s major research funding agencies That content comes from using the summit’s deliberations to guide systematic reviews of both the SCM research literature and Canadian institutional mechanisms that are geared towards building knowledge through research. The major conclusions from those reviews can be summarized as follows: While the research literature to date has yielded useful insights to inform the pursuit of SCM excellence, several research questions of immense practical importance remain unanswered or, at best, inadequately answered The body of research required to answer those questions will have to focus on what the summit’s first White Paper presented as four highly impactful levers that SCM executives must expertly handle to attain excellence: collaboration; information; technology; and talent The proposed research agenda can be pursued in ways that achieve the two inter-­‐related goals of creating new actionable knowledge and building the capacity of today’s students to become tomorrow’s practitioners and contributors to ongoing knowledge growth in the SCM field This White Paper’s details underlying these conclusions build on the information presented in the summit’s first White Paper. That is, while the first White Paper (White Paper 1) identified general SCM themes for which the research needs are most urgent, this White Paper goes further along the path of industry-academia knowledge co-creation. It does so by examining and articulating those needs against the backdrop of available research findings, translating the needs into specific research projects that should be pursued, and providing guidelines for how those projects can be carried out

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Organizational readiness for implementation of Supply Chain Analytics

Supply chains today are amassed with data. To remain competitive in a global economy, supply chain organizations need to constantly derive meaningful information from this plethora of data and make critical business decisions. This process is also referred to as Supply Chain Analytics (SCA). This paper attempts to measure the readiness of organizations to implement Business Analytics – a more generic form of SCA. The results were derived from the survey analysis of 112 respondents in 7 countries from various industries and professional backgrounds. This survey analyzed organizations in four broad categories – standardized and integrated data, well-established infrastructure, sound technical and non-technical expertise and the organizational culture and strategy – and attempted to determine their readiness for implementing Analytics in the organization

A Causal Comparative Analysis of Leveraging the Business Analytical Capabilities and the Value and Competitive Advantages of Mid-level Professionals Within Higher Education

The purpose of this quantitative causal-comparative study is an empirical examination of the differences in business intelligence capability and the value and competitive advantage of mid-level higher education academia professionals from community colleges, four-year public, and four-year private institutions within the United States. Institutions of higher education have an overabundant amount of student data that is often inaccessible and underutilized. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Management Information Systems/Decision Support Systems theory, using two-way ANOVA analysis, this research examined factors to understand the mastery of readiness for mid-level professionals in higher education institutions to embrace digital technologies and resources to develop a culture of digital transformation. This study applied the Business Analytics Capability Assessment survey responses from 176 mid-level higher education professionals, from community colleges, four-year private, and four-year public institutions, to understand how higher education professionals use Business Intelligence Analytics (BIA) and Big Data (BD) to improve the organization, operational business decisions, and data management strategies to provide actionable insights. This study found no significance between the type of institution that has business intelligence capability and the value and competitive advantage. A significant difference with a medium effect was identified between the Business Analytics Capability and the Value and Competitive Advantage for mid-level professionals who do and do not utilize BIA and BD resources. Therefore, this study calls for future research to understand how successful institutions have implemented BIA and BD tools and how higher education is shaped on a macro level

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Business intelligence readiness factors for higher education institution

Higher Education Institution (HEI) have embarked on the new style of decision-making with the aim to enhance the speed and reliability of decision-making capabilities. One of the hardest challenges in implementing Business Intelligence (BI) is the organization’s readiness towards adopting and implementing BI systems. Currently, few published studies have examined BI readiness in HEI environment. Seeing this challenge, this study aims to contribute in determining the BI readiness factors in HEI specifically in the deployment strategies. Through inductive attention to BI in HEI environment, three broad factors have been identified: a) Organizational – that concerning on business strategies, process and structure, b) Technology – involves the BI system and knowledge for managing including the sources and c) Social – the culture within organization that may influence decision-making and its processes. This paper also makes recommendations for future research

Business intelligence readiness factors for higher education institution

Higher Education Institution (HEI) have embarked on the new style of decision-making with the aim to enhance the speed and reliability of decision-making capabilities. One of the hardest challenges in implementing Business Intelligence (BI) is the organization’s readiness towards adopting and implementing BI systems. Currently, few published studies have examined BI readiness in HEI environment. Seeing this challenge, this study aims to contribute in determining the BI readiness factors in HEI specifically in the deployment strategies. Through inductive attention to BI in HEI environment, three broad factors have been identified: a) Organizational – that concerning on business strategies, process and structure, b) Technology – involves the BI system and knowledge for managing including the sources and c) Social – the culture within organization that may influence decision-making and its processes. This paper also makes recommendations for future research