Completing a PhD in business and management: a brief guide to doctoral students and universities

Purpose – Despite the ever-growing number of PhD students all over the world, there remain significant doubts about whether entering students in business and management disciplines fully understand the process of producing a PhD thesis, defending it and developing a coherent publication strategy. Hence, the purpose of this paper is to offer some guidance on what it takes to successfully complete a doctoral research thesis. Design/methodology/approach – The arguments and guidance presented in this viewpoint paper are drawn on the authors’ collective supervision and doctoral examination experiences. Findings – The paper presents guidelines on three key issues related with the doctoral completion: choosing a research problem; demonstrating rigour and quality; developing a publication strategy. Originality/value – The content presented in this paper would be valuable aide to those pursuing doctoral research

The changing landscape of IS project failure: an examination of the key factors

YesInformation systems (IS) project failure has been a recurring problem for decades. The purpose of this paper is twofold: first, to examine the key factors that influence project failure and an analysis of the major areas that can have a significant impact on success; and second, to explore some of the key aspects that have an impact on project management performance from the practitioner perspective and discusses the problems faced by organizations in the closer integration of change and project management. Design/methodology/approach: This study critically reviews the IS failure literature developing a synthesized view of the key issues and common reasons for projects to fail. The approach taken in this study is one that focuses on a number of key questions that pull together the relevant themes in this genre of research whilst highlighting many of the implications for practitioners and organizations alike. Findings: Key questions remain on the underlying causes of instances of poor project management as an IS failure factor. The literature has omitted to develop a deeper analysis of the associations between failure factors and the potential causal relationships between these factors. The realization of project benefits relies on the success of both change and project management yet the formal integration of these two disciplines is constrained by separate standards bodies and an immature body of research. Research limitations/implications: This study is limited by its theoretical nature lacking an empirical element to provide a deeper analysis of IS failure factors and their interrelationships. This specific area is a recommendation for future research, where causal relationships between failure factors could be developed via a mathematic-based method such as interpretive structural modeling. Practical implications: With failure rates of IS projects still unacceptably high after decades of attempts to significantly change outcomes, a deeper analysis of this topic is required. The research gaps and recommendations for practitioners highlighted in this study have the potential to provide valuable contributions to this topic of research. Originality/value: The intent of this study is to present a new perspective of this genre of IS research that develops the main arguments and gaps in the literature from the practitioner viewpoint

Method variation in calculating perceived change

Motivated by findings in the literature suggesting that error attributed to measures used in generating retrospective reports are excessive, this study explores error attributed to methods that individuals use for calculating change retrospectively. Preliminary findings indicate that method variation is present which, in turn, affects the reported change scores (i.e., the scores varied as a function of the calculation method used). These findings suggest that the accuracy and comparability of retrospective reporting might be improved if one controls for inter-individual calculation method variation. A brief discussion of the implications of the results along with suggestions for future research is provided

Sales Management: A Global Perspective

As sales managers are encouraged to manage increasingly global territories, the art of selling becomes complicated and the rules of negotiation more diverse. This absorbing book considers the many facets of cross-cultural sales management, to provide salespeople and managers with a guide to making the most of the global sales force. Included in the book are ten international case studies designed to give sales students, salespeople and their managers an explanation of diverse cultures and the dilemmas, situations and opportunities that arise when selling across borders. … [From Amazon.com]https://digitalcommons.odu.edu/marketing_books/1001/thumbnail.jp

Social Media: The Good, the Bad, and the Ugly

International audienceBook Front Matter of LNCS 984

Challenges and opportunities in the digital era: 17th IFIP WG 6. 11 conference on e-business, e-services, and e-society, I3E 2018, Kuwait City, Kuwait, October 30 - November 1, 2018, proceedings

International audienceBook Front Matter of LNCS 1119

Relating big data and data quality in financial service organizations

<p>Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex relationship between big data and data quality. We do this by comparing the big data characteristics with data quality dimensions. Data quality has been researched for decades and there are well-defined dimensions which were adopted, whereas big data characteristics represented by eleven V’s were used to characterize big data. Literature review and ten cases in financial service organizations were invested to analyze the relationship between data quality and big data. Whereas the big data characteristics and data quality have been viewed as separated domain ours findings show that these domains are intertwined and closely related. Findings from this study suggest that variety is the most dominant big data characteristic relating with most data quality dimensions, such as accuracy, objectivity, believability, understandability, interpretability, consistent representation, accessibility, ease of operations, relevance, completeness, timeliness, and value-added. Not surprisingly, the most dominant data quality dimension is value-added which relates with variety, validity, visibility, and vast resources. The most mentioned pair of big data characteristic and data quality dimension is Velocity-Timeliness. Our findings suggest that term ‘big data’ is misleading as that mostly volume (‘big’) was not an issue and variety, validity and veracity were found to be more important.</p