Search results

The purpose of this paper is to develop a methodology for building a causality process to dynamically investigate system performance linkages, as implied by a strategic frame such as a quality management frame or the balance scorecard frame.

The causality process develops aggregated process control tools in a data mining structure. The method identifies significant performance improvements by means of Cumulative Sum control charts, uses binary variables to mark them and time bounded search cycles as dictated by the causality constraints.

The methodology was implemented in a manufacturing enterprise and thus enables to better understand causality at the operational (tactical) level as well as at the strategic level.

Additional research issues for further investigation are expressed by questions such as follows: what other data mining tools may be appropriate? How to methodically construct learning performance indicators?

The causality process and its assisting process control techniques are suitable to a variety of managerial situations where techniques for monitoring and testing given frames of hypothesized performance linkages are needed.

The methodology enables an individual organization to dynamically investigate its performance in order to improve the implementation of its strategy.

The purpose of this paper is to explore the role of qualitative research in causality, with particular emphasis on process causality. In one paper, it is not possible to discuss all the issues of causality, but the aim is to provide useful ways of thinking about causality and qualitative research. Specifically, a brief overview of the regularity theory of causation is provided, qualitative research characteristics and ontological and epistemological views that serve as a potential conceptual frame to resolve some tensions between quantitative and qualitative work are discussed and causal processes are explored. This paper offers a definition and a model of process causality and then presents findings from an exploratory study that advanced the discussion beyond the conceptual frame.

This paper first conceptually frames process causality within qualitative research and then discusses results from an exploratory study that involved reviewing literature and interviewing expert researchers. The exploratory study conducted involved analyzing multiple years of literature in two top human resource development (HRD) journals and also exploratory expert interviews. The study was guided by the research question: How might qualitative research inform causal inferences in HRD? This study used a basic qualitative approach that sought insight through inductive analysis within the focus of this study.

The exploratory study found that triangulation, context, thick description and process research questions are important elements of qualitative studies that can improve research that involves causal relationships. Specifically, qualitative studies provide both depth of data collection and descriptive write-up that provide clues to cause-and-effect relationships that support or refute theory.

A major conclusion of this study is that qualitative research plays a critical role in causal inference, albeit an understated one, when one takes an enlarged philosophical view of causality. Equating causality solely with variance theory associated with quantitative research leaves causal processes locked in a metaphoric black box between cause and effect, whereas qualitative research opens up the processes and mechanisms contained within the box.

This paper reframed the discussion about causality to include both the logic of quantitative studies and qualitative studies to demonstrate a more holistic view of causality and to demonstrate the value of qualitative research for causal inference. Process causality in qualitative research is added to the mix of techniques and theories found in the larger discussion of causality in HRD.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

Through a detailed review of Literature, the purpose of this paper is to provide insights into the state-of-the-art research about the process of information technology business value (ITBV) creation, a less-traversed direction in ITBV research, from the perspective of causality since a lack of causal reasoning may be disastrous for ITBV creation.

With the help of eight keywords, ten databases were searched which fetched about 415 articles of which 22 were selected based on their relevance and proved as the base papers for classifying available literature. A further forward and reverse search fetched an additional 34 articles, resulting in a total of 56 articles which were reviewed in detail.

The five main categories of literature which emerged are ITBV (General), ITBV benefits, mediating factors and synergy (which use of organization dynamic capability (ODC) as first stream of ITBV research), and IT-enabled organizational transformation (ITOT as second stream). ODC is fairly mature, however, ITOT will benefit from a further research. Research in the ITBV (General) category suggests the development of dynamic models as opposed to the prevalent static models of ITBV creation.

For the period 1990-2008, only the most important articles were included in the study and hence certain pre-2008 articles’ view might have been overlooked.

The literature review will give practitioners a perspective to look at specific areas in the context of their organization to develop capabilities which will lead to enhanced ITBV creation.

This review focusses on ITBV creation and helps move toward building of a dynamic holistic model of ITBV creation by providing only a bird’s eye view is provided of the most important articles from 1990 to 2008 but a comprehensive view of literature from 2008 to mid-2014.

Interventions to support gender equality in organisations are often unsuccessful. Stakeholders disagree about the causes and problem definition of gender equality or pay lip service to the principle of gender equality, but fail to implement gender equality in practice. The purpose of this paper is to examine participatory modelling as an intervention method to support stakeholders in: reaching a shared problem definition and analysis of gender inequality; and identifying and implementing policies to tackle gender inequality.

The authors apply participatory modelling in case studies on impediments to women’s careers in two Dutch universities.

This study shows that participatory modelling supported stakeholders’ identification of the self-reinforcing feedback processes of masculinity of norms, visibility of women and networking of women and the interrelatedness between these processes. Causal loop diagrams visualise how the feedback processes are interrelated and can stabilise or reinforce themselves. Moreover, they allow for the identification of possible interventions.

Further testing of the causal loop diagrams by quantifying the stocks and the flows would validate the feedback processes and the estimated effects of possible interventions.

The integration of the knowledge of researchers and stakeholders in a causal loop diagram supported learning about the issue of gender inequality, hereby contributing to transformative change on gender equality.

The originality of the paper lies in the application of participatory modelling in interventions to support gender equality.

The purpose of this paper is to address the following question: How do strategy implementation obstacles relate to each other and affect strategy implementation?

The research methodology is qualitative and based on an extensive review of the literature and on an in-depth case study analysis.

This paper draws two main conclusions. The first is that the many obstacles that impact the strategy implementation process can interact and be strongly interrelated in dynamic and complex manners. The second is that obstacles can lead to and reinforce other obstacles, eventually forming long chains of blockages.

Strategy implementation remains a difficult task with improbable success. This paper provides a contribution to an explanation on why so many strategy implementation efforts fail. It is one of the very few papers addressing the issue of the relationships between strategy implementation obstacles.

The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and definition in social marketing.

The paper is an illustrative case study approach, highlighting three real-world exemplars of causal modelling for wicked problem definition.

The findings show how the traditional linear research methods of social marketing are not sensitive enough to the dynamics and complexities of wicked problems. A shift to non-linear causal modelling techniques and methods, using interaction as the unit of analysis, provides insight and understanding into the chains of causal dependencies underlying social marketing problems.

This research extends the application of systems thinking in social marketing through the illustration of three non-linear causal modelling techniques, namely, collective intelligence, fuzzy cognitive mapping and system dynamics modelling. Each technique has the capacity to visualise structural and behavioural properties of complex systems and identify the central interactions driving behaviour.

Non-linear causal modelling methods provide a robust platform for practical manifestations of collaborative-based strategic projects in social marketing, when used with participatory research, suitable for micro, meso, macro or systems wide interventions.

The paper identifies non-linear causality as central to wicked problem scoping identification, documentation and analysis in social marketing. This paper advances multi-causal knowledge in the social marketing paradigm by using fuzzy, collective and interpretative methods as a bridge between linear and non-linear causality in wicked problem research.

Academic qualitative researchers have been criticized for rejecting the idea that their research can establish causality while market and social researchers, with their realist and pragmatic approach to research, take for granted that it can. This paper aims to explore the ability of qualitative research to determine cause and effect in terms of market and social phenomena.

The literature on causality in qualitative research is reviewed and discussed. The discussion is further informed by the author’s own experience of undertaking commercial and academic market and social qualitative research over the past 33 years.

In qualitative market and social research, the determination of causality is often needed but rarely discussed. This paper explores this occurrence and brings to the fore, via discussion and the use of example, the ways in which causality can be determined by qualitative research.

A determination of what events bring about predictable changes in social and market environments can be established via qualitative research particularly at a probabilistic level of causality. This implies that policymakers should give a greater emphasis to qualitative findings than then sometimes do at the moment.

Causality in market and social research is rarely discussed by practitioners but is nevertheless a premise of much of the qualitative research that is undertaken. This paper is therefore distinctive in that it examines whether this premise is justifiable.