Adrian Thornhill

Bio: Adrian Thornhill is an academic researcher from University of Gloucestershire. The author has contributed to research in topics: Human resource management & Strategic human resource planning. The author has an hindex of 23, co-authored 47 publications receiving 27983 citations.

Research Methods for Business Students

Abstract: How to use this book Guided tour Preface Contributors 1 The nature of business and management research and structure of this book 2 Formulating and clarifying the research topic 3 Critically reviewing the literature 4 Understanding research philosophies and approaches 5 Formulating the research design 6 Negotiating access and research ethics 7 Selecting samples 8 Using secondary data 9 Collecting primary data through observation 10 Collecting primary data using semi-structured, in-depth and group interviews 11 Collecting primary data using questionnaires 12 Analysing quantitative data 13 Analysing qualitative data 14 Writing and presenting your project report Appendices Glossary Index

Research Methods for Business Students (5th edn)

Abstract: " " I'm confused by all these different philosophies" " I've got my data; what do I write first?" ΠK then, open this book to read: * Regular checklists and ...

Business Research Methods

Abstract: written last as this must include a flavour of results, don’t repeat phrases from the main text. If we don’t get the reader’s interest in the short abstract, they are unlikely to read the rest of the report. Introduction, must immediately grab the reader’s attention, often by a dramatic statement of the problem or situation to be researched. Background, usually starts with a broad picture and gradually refines it to the narrow focus of the research (a filter) Literature review, see the earlier chapter on this subject Research objective and method justification, most of this book has been about this section, but it must not appear as a stand-alone section. Every section including this one should follow logically from the previous one and lead naturally to the next. So, for example, the literature review section should end with a direction for the primary research, which is then picked up in the research method section. Findings, try to offer the findings of your research in as pure a form as possible. This doesn’t mean giving raw data, it means finding a way to present that data so the characteristics of the data are clear to the reader, without interpreting the data, so that the reader is dependent on your view and cannot see the data for themselves. Visual methods such as charts and tables can summarise and present data effectively, but not pages and pages of them which soon cause overload. Discussion and analysis, this is the real test of your ability to synthesise what you found in the literature review and in your primary research and to pull out from that synthesis what seem to you to be the most important points. It is not a place to put any description. Writing should be clear but intense – all sentences must add value. Presenting research reports

Survey response rate levels and trends in organizational research

Abstract: This study examines the response rates for surveys used in organizational research. We analysed 1607 studies published in the years 2000 and 2005 in 17 refereed academic journals, and we identified 490 different studies that utilized surveys. We examined the response rates in these studies, which covered more than 100,000 organizations and 400,000 individual respondents. The average response rate for studies that utilized data collected from individuals was 52.7 percent with a standard deviation of 20.4, while the average response rate for studies that utilized data collected from organizations was 35.7 percent with a standard deviation of 18.8. Key insights from further analysis include relative stability in response rates in the past decade and higher response rates for journals published in the USA. The use of incentives was not found to be related to response rates and, for studies of organizations, the use of reminders was associated with lower response rates. Also, electronic data collection efforts (e.g. email, phone, web) resulted in response rates as high as or higher than traditional mail methodology. We discuss a number of implications and recommendations.

Structural Equation Modeling in Information Systems Research Using Partial Least Squares

Abstract: Empirical studies that use structural equation modeling (SEM) are widespread in information systems research. During the last few years, the component-based approach partial least squares (PLS) for testing structural models has become increasingly popular. At the same time, this approach's limitations have become a greater concern. Some researchers even suggest using alternative approaches that are considered superior to PLS. However, we believe that PLS is an adequate choice if the research problem meets certain characteristics and the technique is properly used. Thus, the intention of this article is to resolve potential uncertainties that researchers intending to use PLS might have. Consequently, we provide a nontechnical overview of PLS and outline the ongoing discourses on SEM in general and the PLS approach in particular. Furthermore, we present a basic framework for empirical research applying PLS as well as a detailed explanation of the different process steps. Finally, examples of information systems research using PLS are summarized to demonstrate its beneficial application and the appropriateness of the proposed framework. This article can serve as a helpful guide for inexperienced researchers applying PLS for the first time, but also as a reference guide for researchers with a better understanding of the field. Keywords: structural equation modeling, partial least squares, information systems research I. INTRODUCTION The information systems (IS) discipline examines socioeconomic systems that are characterized by the interplay between hardware and software on the one hand, as well as individuals, groups, and organizations on the other. For example, technology adoption, acceptance, and success, as well as the conditions under which these can be achieved are typical issues that are addressed by IS research. These research fields are similar in that their investigation requires the researcher to cope with constructs such as the beliefs, perceptions, motivation, attitude, or judgments of the individuals involved. These constructs are usually modeled as latent variables (LVs) that can be measured only through a set of indicators. Structural equation models describe the relationships between several of these LVs. A number of algorithms and software programs are available to estimate their relationships based on a dataset. Among these algorithms, the partial least squares (PLS) algorithm has become increasingly popular both in IS research and in other disciplines such as marketing (Albers 2010; Henseler et al. 2009) or strategic management (Hulland 1999). However, reminders of this approach's limitations have recently become more prominent. Consequently, researchers opt for a more careful application of PLS. Especially its statistical power at small sample sizes, the overall model fit, as well as the misspecification of measurement models have been the focus of recent discussions. To resolve the uncertainties that researchers intending to use PLS might have, we investigate current discourses on the PLS approach. In the following sections, we: * demonstrate the increasing popularity of PLS in the IS research community * outline the PLS approach by providing a nontechnical overview and reflecting on the ongoing discussion on structural equation modeling (SEM) in general and on PLS in particular * discuss differences between PLS and covariance-based approaches * present a basic framework for empirical research applying the PLS approach * provide examples of its beneficial application in IS research As a result, this paper helps SEM beginners and advanced researchers to make an informed decision about whether to use SEM or other alternative approaches to SEM. Even more, we present up-to-date recommendations on how to apply PLS appropriately. Section II demonstrates the increasing popularity of PLS for SEM in IS research by conducting a systematic review of literature that appeared in two prestigious IS journals during the last fifteen years. …

Using case studies in research

Abstract: Draws heavily on previous established research in an attempt to distil the key aspects of case study research in such a way as to encourage new researchers to grapple with and apply these. Explains when case study can be used, research design, data collection and data analysis, offering suggestions for drawing on the evidence in writing a report or dissertation. Briefly reviews alternative perspectives on the subject.