This issue of EJIS includes six articles. The first article ‘Digital business reporting standards: mapping the battle in France’, co-authored by Véronique Guilloux from UPEC France, Joanne Locke from Birmingham Business School and Alan Lowe from Aston Business School, traces the existent relationship dynamics and pressures between two competing business reporting and ICT data standards in France; namely the Electronic Data Interchange for Administration, Commerce and Transport (EDIFACT) and eXtensible Business Reporting Language (XBRL). The study mobilizes Actor Network Theory (ANT) to examine the impact of certain actors and events on adoption decisions made by French government bodies and institutions and the path these standards have followed. The study, among other things, shows how regulator actors play an important role as adopters in the path that this relationship follows. A new standard may then challenge the position of an existing incumbent standard under certain conditions. The study also shows the influence that certain networks and structures may have on such a standards adoption relationship, while questioning the differentiating attitudes and whether speed of development of the standard is more important than its legitimacy in relation to adopters’ decisions.
The second article ‘Top management support in multiple-project environments: an in-practice view’ by Amany Elbanna from Royal Holloway University of London, also uses the ANT framework but this time to challenge assumptions related to the top management's steady and consistent support as being a critical factor for the success of an IS project. A novel aspect of this research is that the question is raised in a multi-project setting. The study shows that top management support is not as constant as previously assumed, nor unidirectional or passively available, but rather, it is constructed through projects’ efforts to attract top management's attention. The project's actors most often change over time. Similarly, a project's continuation and success depends on its active mobilization of local networks hence the call addressed to project managers and practitioners to build and strengthen their project's local network and continue efforts, despite the lack of top management attention that may be witnessed.
Our third article, titled ‘Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts’, is co-authored by Tamara Dinev and Paul Hartfrom from Florida Atlantic University, Heng Xu from Pennsylvania State University, and Jeff H Smith from Miami University, and addresses the multidimensionality of the concept of information privacy. The study mobilizes perceived privacy as a dependent variable for information privacy. Perceived privacy in the proposed research model is the outcome of two variables namely perceived information control and perceived risk. Perceived information control uses three tactics to achieve control: anonymity, secrecy, and confidentiality. Perceived risk is the outcome of the perceived benefits of information disclosure, the information's sensitivity, the importance of information transparency, and matching regulatory expectations. The model is supported and shows strong relevance using data collected from 192 responses to an administered survey.
The fourth article titled ‘Sympathy or strategy: social capital drivers for collaborative contributions to the IS community’ presented by Matthias Trier from Copenhagen Business School and Judith Molka-Danielsen from Molde University College investigates researchers’ structural patterns of academic collaboration and co-authorships using a social network perspective. It does so by taking into considering different styles and profiles of research in the IS field that also involve citation and publication preferences. Utilizing analytical dimensions suggested by social capital theory, the study shows that inter-organizational relationships form, to a large extent, a central backbone in scientific productions, whereas at the periphery, national relationships dominate. It also finds that structural and relational social capital dimensions were perceived as being critical. Finally, the study also establishes that a low level of network centrality is closely related with a topic-oriented disposition.
The fifth article ‘A method for taxonomy development and its application in information systems’ written by Robert C Nickerson from San Francisco State University, Upkar Varshney from Georgia State University and Jan Muntermann from University of Göttingen advances a methodology for developing a taxonomy adequate for the IS field. The approach for taxonomy development follows a design science approach and starts by setting meta characteristics, then following an iterative process of empirical-to-conceptual and conceptual-to-empirical pattern new dimensions under a particular taxonomy are created. The taxonomy then consists of set of dimensions, each consisting of mutually exclusive and collectively exhaustive characteristics such that each object under consideration has one and only one characteristic for each dimension. The article also suggests objective and subjective ending conditions that would ensure that the new dimensions introduced under a particular taxonomy meet the criteria and conditions desired. The taxonomy development method is illustrated using the case of mobile applications.
The sixth and final article in this issue, ‘Can we have fun @ work? The role of intrinsic motivation for utilitarian systems’, is co-authored by Jennifer E Gerow from Virginia Military Institute, Ramakrishna Ayyagari from University of Massachusetts, Jason Bennett Thatcher, and Philip L Roth from Clemson University. It runs a meta-analysis over 185 user acceptance studies to question whether the nature of the system influences intrinsic motivation's relationship with users’ perceptions, intentions, and use of that system. According to the authors, the system could have either a hedonic, utilitarian, or mixed nature. The impact of both intrinsic and extrinsic motivation over perceived enjoyment influences the perceived ease of use. Hence, the recommendation to system developers is to pay attention to such motivational features in the design phase that engage users across all system types. This is particularly relevant since the meta-analysis revealed that the relationship between intrinsic motivation and the traditional TAM constructs was similar across system types.
Qualitative, quantitative and mixed methods dissertations
What are they and which one should I choose?
In the sections that follow, we briefly describe the main characteristics of qualitative, quantitative and mixed methods dissertations. Rather than being exhaustive, the main goal is to highlight what these types of research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you. After reading about these three types of dissertation, we highlight some of the academic, personal and practical reasons why you may choose to take on one type over another.
Types of dissertation
Whilst we describe the main characteristics of qualitative, quantitative and mixed methods dissertations, the Lærd Dissertation site currently focuses on helping guide you through quantitative dissertations, whether you are a student of the social sciences, psychology, education or business, or are studying medical or biological sciences, sports science, or another science-based degree. Nonetheless, you may still find our introductions to qualitative dissertations and mixed methods dissertations useful, if only to decide whether these types of dissertation are for you. We discuss quantitative dissertations, qualitative dissertations and mixed methods dissertations in turn:
When we use the word quantitative to describe quantitative dissertations, we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques. Quantitative research takes a particular approach to theory, answering research questions and/or hypotheses, setting up a research strategy, making conclusions from results, and so forth. Classic routes that you can follow include replication-based studies, theory-driven research and data-driven dissertations. However, irrespective of the particular route that you adopt when taking on a quantitative dissertation, there are a number of core characteristics to quantitative dissertations:
They typically attempt to build on and/or test theories, whether adopting an original approach or an approach based on some kind of replication or extension.
They answer quantitative research questions and/or research (or null) hypotheses.
They are mainly underpinned by positivist or post-positivist research paradigms.
They draw on one of four broad quantitative research designs (i.e., descriptive, experimental, quasi-experimental or relationship-based research designs).
They try to use probability sampling techniques, with the goal of making generalisations from the sample being studied to a wider population, although often end up applying non-probability sampling techniques.
They use research methods that generate quantitative data (e.g., data sets, laboratory-based methods, questionnaires/surveys, structured interviews, structured observation, etc.).
They draw heavily on statistical analysis techniques to examine the data collected, whether descriptive or inferential in nature.
They assess the quality of their findings in terms of their reliability, internal and external validity, and construct validity.
They report their findings using statements, data, tables and graphs that address each research question and/or hypothesis.
They make conclusions in line with the findings, research questions and/or hypotheses, and theories discussed in order to test and/or expand on existing theories, or providing insight for future theories.
If you choose to take on a quantitative dissertation, go to the Quantitative Dissertations part of Lærd Dissertation now. You will learn more about the characteristics of quantitative dissertations, as well as being able to choose between the three classic routes that are pursued in quantitative research: replication-based studies, theory-driven research and data-driven dissertations. Upon choosing your route, the Quantitative Dissertations part of Lærd Dissertation will help guide you through these routes, from topic idea to completed dissertation, as well as showing you how to write up quantitative dissertations.
Qualitative dissertations, like qualitative research in general, are often associated with qualitative research methods such as unstructured interviews, focus groups and participant observation. Whilst they do use a set of research methods that are not used in quantitative dissertations, qualitative research is much more than a choice between research methods. Qualitative research takes a particular approach towards the research process, the setting of research questions, the development and use of theory, the choice of research strategy, the way that findings are presented and discussed, and so forth. Overall, qualitative dissertations will be very different in approach, depending on the particular route that you adopt (e.g., case study research compared to ethnographies). Classic routes that you can follow include autoethnographies, case study research, ethnographies, grounded theory, narrative research and phenomenological research. However, irrespective of the route that you choose to follow, there are a number of broad characteristics to qualitative dissertations:
They follow an emergent design, meaning that the research process, and sometimes even the qualitative research questions that you tackle, often evolve during the dissertation process.
They use theory in a variety of ways - sometimes drawing on theory to help the research process; on other occasions, using theory to develop new theoretical insights; sometimes both - but the goal is infrequently to test a particular theory from the outset.
They can be underpinned by one of a number of research paradigms (e.g., interpretivism, constructivism, critical theory, amongst many other research paradigms).
They follow research designs that heavily influence the choices you make throughout the research process, as well as the analysis and discussion of 'findings' (i.e., such research designs differ considerably depending on the route that is being followed, whether an autoethnography, case study research, ethnography, grounded theory, narrative research, phenomenological research, etc.).
They try to use theoretical sampling - a group of non-probability sampling techniques - with the goal of studying cases (i.e., people or organisations) that are most appropriate to answering their research questions.
They study people in-the-field (i.e., in natural settings), often using multiple research methods, each of which generate qualitative data (e.g., unstructured interviews, focus groups, participant observation, etc.).
They interpret the qualitative data through the eyes and biases of the researcher, going back-and-forth through the data (i.e., an inductive process) to identify themes or abstractions that build a holistic/gestalt picture of what is being studied.
They assess the quality of their findings in terms of their dependability, confirmability, conformability and transferability.
They present (and discuss) their findings through personal accounts, case studies, narratives, and other means that identify themes or abstracts, processes, observations and contradictions, which help to address their research questions.
They discuss the theoretical insights arising from the findings in light of the research questions, from which tentative conclusions are made.
If you choose to take on a qualitative dissertation, you will be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation. However, we have not yet launched a dedicated section to qualitative dissertations within Lærd Dissertation. If this is something that you would like us to do sooner than later, please leave feedback.
Mixed methods dissertations
Mixed methods dissertations combine qualitative and quantitative approaches to research. Whilst they are increasingly used and have gained greater legitimacy, much less has been written about their components parts. There are a number of reasons why mixed methods dissertations are used, including the feeling that a research question can be better addressed by:
Collecting qualitative and quantitative data, and then analysing or interpreting that data, whether separately or by mixing it.
Conducting more than one research phase; perhaps conducting qualitative research to explore an issue and uncover major themes, before using quantitative research to measure the relationships between the themes.
One of the problems (or challenges) of mixed methods dissertations is that qualitative and quantitative research, as you will have seen from the two previous sections, are very different in approach. In many respects, they are opposing approaches to research. Therefore, when taking on a mixed methods dissertation, you need to think particularly carefully about the goals of your research, and whether the qualitative or quantitative components (a) are more important in philosophical, theoretical and practical terms, and (b) should be combined or kept separate.
Again, as with qualitative dissertations, we have yet to launch a dedicated section of Lærd Dissertation to mixed methods dissertations. However, you will be able to learn about many of the quantitative aspects of doing a mixed methods dissertation in the Quantitative Dissertations part of Lærd Dissertation. You may even be able to follow this part of our site entirely if the only qualitative aspect of your mixed methods dissertation is the use of qualitative methods to help you explore an issue or uncover major themes, before performing quantitative research to examine such themes further. Nonetheless, if you would like to see a dedicated section to mixed methods dissertations sooner than later, please leave feedback.