Investigating Critical Success Factors of Project Management in Global Software Development: A Work in Progress

Global software development (GSD) business model has gained recognition over the years for achieving competitiveness in the global market. However, its implementation is not easy due to its complex nature and the various challenges it faces. Project management is a vital area in software development with significant impact to the GSD process. Companies adopt GSD without knowing its implications which lead to failure of their project management processes. Existing project management practises do not address the core issues of GSD, which makes the process more intricate. This research paper investigates the project management practices in GSD and identifies its critical success factors with the development of a framework that will facilitate software companies to implement GSD successfull

A Framework to Assess the Critical Success Factors for Cloud Enterprise Resource Planning Adoption in Small and Medium-sized Enterprises

Enterprise resource planning (ERP) is a well-known business management system used for improving effectiveness in organisations. In the current digital era, cloud ERP systems have evolved which are taking precedence over the traditional ERP due to convenience of remote information access in real-time with benefits of cost saving, flexibility and scalability. These systems are especially helpful to SMEs which usually are constrained in resources. However, there have not been many studies that look at the critical success factors for cloud ERP adoption in SMEs. This paper develops an integrative framework using technology-organisation-environment (TOE) and unified theory of acceptance and use of technology (UTAUT) models to investigate the individual, environmental, technological and organisational levels of cloud ERP adoption in SMEs for identifying factors for success. The findings will provide new insights on cloud ERP adoption and will help both academia and practitioners increase understanding for future research and implementatio

CRITICAL ENTERPRISE SOFTWARE CONTRACTING ISSUES: RIGHTS, ASSURANCES AND RESPONSIBILITIES

Unfavorable contractual agreement can be detrimental to the well-being of an organization. Often software contracts are written to favor the vendor and their terminology is vague and in a high-level language that can make organizations vulnerable. Among all IT applications, Enterprise Software is particularly critical due to integrating various critical business processes. In addition, ES implementations are among the most expensive types of IT implementations. Thus, ES contracting mistakes can be particularly costly for organizations

Harvesting Wisdom on Social Media for Business Decision Making

The proliferation of social media provides significant opportunities for organizations to obtain wisdom of the crowds (WOC)-type data for decision making. However, critical challenges associated with collecting such data exist. For example, the openness of social media tends to increase the possibility of social influence, which may diminish group diversity, one of the conditions of WOC. In this research-in-progress paper, a new social media data analytics framework is proposed. It is equipped with well-designed mechanisms (e.g., using different discussion processes to overcome social influence issues and boost social learning) to generate data and employs state-of-the-art big data technologies, e.g., Amazon EMR, for data processing and storage. Design science research methodology is used to develop the framework. This paper contributes to the WOC and social media adoption literature by providing a practical approach for organizations to effectively generate WOC-type data from social media to support their decision making

Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand

Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed

Cognitive biases in developing biased Artificial Intelligence recruitment system

Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people’s role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed

The influence of personal knowledge management on individual decision making in health care medical treatment

In the age of technology, individuals are often confronted with considerable volumes of information when reading through literature relating to healthcare medical treatment. This may create confusion and hinder their decision-making process and they may not know what to do about their situation. With this in mind, this study will investigate how personal knowledge management (PKM) can help individuals better manage complex healthcare issues through well-informed decisions when facing healthcare medical treatment. Grounded action learning, an integration of grounded theory and action learning is adopted for this study. A framework for a PKM-based decision making training program format has been proposed based on action learning methods. Data collection and analysis is based on grounded theory approaches. This study is expected to provide new insights for PKM implementation to help individuals manage information overload and improve their information literacy skills as well as knowledge management (KM) capabilities when confronting health-related decisions

Development of a Taxonomy to be used by Business-IT Alignment Researchers

The nexus between Business and IT research is complex. Due to extended research over time, the context of business-IT alignment has resulted in many different conceptualisations that can be applied to ongoing research. It is challenging to select and adopt a suitable approach to study business-IT alignment across any given field due to the variability of the existing conceptualisations. This study reviews the existing literature to identify alignment conceptualisations and contributes to both theory and practice. Theoretically, through the uncovering of gaps in the literature a taxonomy has been developed which can be used as a guide to select an appropriate alignment lens for business-IT alignment studies. In practice, it is expected that this taxonomy will be beneficial for conceptualising the structure and philosophies underpinning future alignment studies. To validate the taxonomy, the paper presents a case study in healthcare applying the developed taxonomy to investigate alignment of big data in health

An Ontology-based Approach for Model Representation, Sharing and Reuse

Although Decision Support Systems (DSS) play a dominant role in organizing data and models, its capability in supporting decision makers in collaborating distributed environments is still limited when it comes to the selection, sharing, and re-use of models. For mathematical models to be shared and reused, mechanisms are needed for understanding, implementing, modifying, discovering, selecting, engaging, and composing models. At a fundamental level, model representation will need to extend beyond model structure to include model semantics as well. This research leverages advances in Semantic Web technologies and ontologies to enable sharing and re-using of decision models by providing enriched semantics in collaborative decision making environments. The proposed approach builds on structured modeling (SM) as an underlying modeling formalism and is illustrated using the Web Ontology Language (OWL). A case study demonstrates the viability of the approach for capturing model semantics models using ontologies

Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam

Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology