William Yeoh

Managing the Implementation of Business Intelligence Systems: A Critical Success Factors Framework

The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.

Extending the understanding of critical success factors for implementing business intelligence systems

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource‐intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronioss (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross‐case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems.

Towards a Critical Success Factor Framework for Implementing Business Intelligence Systems: A Delphi Study in Engineering Asset Management Organizations

This paper presents the results of three rounds Delphi study with 15 BI systems experts in the domain of engineering asset management. The study provides a CSF framework that consists of seven dimensions and 22 factors crucial for successful BI system implementation. The seven critical dimensions of CSFs are management commitment and championship, user-oriented change management, business vision, project planning, team skills and composition, data and infrastructure-related dimensions. These findings allow BI stakeholders to optimize their scarce resources on those key areas that are most likely to have an impact on the implementation of the BI systems.

Feature selection for high dimensional imbalanced class data using harmony search

Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks.

Critical Success Factors for the Implementation of Business Intelligence System in Engineering Asset Management Organisations

Much IS literature suggests that various factors play pivotal roles in the implementation of an information system; however, there has been little empirical research about the factors impacting the implementation of business intelligence (BI) systems, particularly in engineering asset management organizations (EAMOs). There is an imperative for a critical success factors (CSFs) approach to enable BI stakeholders to focus on the key issues that leading to successful BI systems implementation. The authors utilised the Delphi method to conduct two rounds of surveys with ten BI system experts of EAMOs domain. Based on the findings, this study identifies ten CSFs that are crucial for implementing BI systems in EAMOs. The paper presents a description and discussion of the CSFs and puts forward recommendations for further research. This study can be valuable to researchers and practitioners who are studying, providing consultancies, planning or implementing BI systems within EAMOs setting.

The influence of organisation culture on E-commerce adoption

Purpose – The purpose of this paper is to empirically examine the influence of different organisational cultures on e-commerce adoption maturity in small- and medium-sized enterprises (SMEs). Design/methodology/approach – The data for this study were gathered using postal survey questionnaire and analysed using quantitative analysis methods. Findings – The result indicates a positive correlation between adhocracy culture and e-commerce adoption. However, those firms with hierarchy cultural characteristics indicate a negative correlation in relation to e-commerce adoption. The organisational culture differences explain these issues. Research limitations/implications – The analysis is conducted in a single country (i.e. Sri Lanka). Initial findings provide a basis for further research in other country. Practical implications – This research reveals the significance of organisational culture and how it influences e-commerce adoption maturity, both positively and negatively. The research findings are useful f...

THE IMPACT OF FEATURE SELECTION: A DATA‐MINING APPLICATION IN DIRECT MARKETING

The capability of identifying customers who are more likely to respond to a product is an important issue in direct marketing. This paper investigates the impact of feature selection on predictive models which predict reordering demand of small and medium-sized enterprise customers in a large online job-advertising company. Three well-known feature subset selection techniques in data mining, namely correlation-based feature selection (CFS), subset consistency (SC) and symmetrical uncertainty (SU), are applied in this study. The results show that the predictive models using SU outperform those without feature selection and those with the CFS and SC feature subset evaluators. This study has examined and demonstrated the significance of applying the feature-selection approach to enhance the accuracy of predictive modelling in a direct-marketing context.

How Does Organizational Culture Affect IS Effectiveness: A Culture-Information System Fit Framework

Previous research has examined the impactof organizational culture(OC) on the implementation ofmany information systems. However, there is a lack ofoverall picture on how OC affects the effectiveness ofdifferent information systems differently. Based on theCompeting Value Framework, this paper proposes acomprehensive framework to explain how the fit betweenorganizational culture and types of IS results in differenttypes of IS effectiveness. This framework can be used bymanagers to create a proper organizational culture that iscompatible with the use of specific information systems.

Benefits and Barriers to Corporate Performance Management Systems

Corporate performance management (CPM) systems using business intelligence technologies can help enterprises monitor and manage business performance. In this research, we explored and presented empirical evidence on the key benefits of, and barriers to, the use of CPM systems through a survey of 283 organisations across North America and China. We identified three key benefits (strategy execution, process efficiency, and fact-based decision-making) and ten inhibiting barriers under respective project and organisational dimensions. Moreover, we found that in regard to the use of CPM systems, Chinese organisations perceived higher benefits, as well as higher barriers, than did their counterparts in North America. The socio-cultural differences between the two regions explain these issues. The research findings are useful for multinational organisations that are planning, or are in the process of implementing or reviewing their CPM systems, as well as for consulting companies that are assisting with such systems implementation in different regions.

A literature analysis of the use of Absorptive Capacity construct in IS research

We analyze the use of ACAP in IS research through a comprehensive literature analysis.We reveal that the majority of the IS research conceptualizes ACAP as a capability.Various misalignments between ACAP conceptualization, operationalization and measurement continue to do a disservice to the accumulated research.The research should help IS researchers to conceptualize and operationalize ACAP appropriately. Since the seminal inception of Absorptive Capacity (ACAP) by Cohen and Levinthal (1990), it has been adopted widely in information systems (IS) research. This paper analyzes the use of ACAP in IS research through a literature analysis of ACAP-related papers published in 52 reputable IS journals from 1990 to 2015. Drawing on a review of the evolution of ACAP, the analyses conducted include: (1) descriptive analysis of ACAP in IS papers; (2) domains of ACAP usage; (3) analysis of hypotheses and propositions to show how ACAP is being used to explain various organizational phenomena in IS research; and (4) analysis of the measures to provide insights into the operationalization of ACAP in IS research. Our findings suggest that while the majority of the research correctly conceptualizes ACAP as a capability, various misalignments between ACAP conceptualization, operationalization and measurement, and the level of analysis in the literature continue to do a disservice to the accumulated research in ACAP. The findings and recommendations should help IS researchers to conceptualize and operationalize ACAP appropriately.