Search results

The effect of dynamic capabilities on operational excellence and the moderating effect of environmental dynamism on the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka were investigated while developing new psychometric scales to assess operational excellence and dynamic capacities constructs.

We followed the exploratory sequential research design with a mixed-method research approach, aligning with the pragmatic research philosophy. Thus, both qualitative and quantitative research methods were followed.

Dynamic capabilities positively affect operational excellence, and environmental dynamism moderates the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka such that when a higher environmental dynamism exists, a weaker positive relationship exists between dynamic capabilities and operational excellence. The two main dimensions of the operational excellence construct are continuous improvement of sustainable operational performance and sustainable competitive advantages. It empirically confirmed that sensing, seizing and reconfiguring capabilities are the three main dimensions of the dynamic capabilities construct.

This study was limited to the apparel industry in Sri Lanka. This research phenomenon should be explored in other industrial sectors worldwide to generalize the findings. The practitioners in the apparel sector may improve the organizational dynamic capabilities to achieve operational excellence and keep a strong positive relationship between dynamic capabilities and operational excellence in a highly dynamic environment if they address out-of-family situations with out-of-the-box thinking.

We generated two new empirical findings: (1) dynamic capabilities positively affect operational excellence, and (2) environmental dynamism moderates the relationship between dynamic capabilities and operational excellence. Also, we introduced validated new scales for assessing operational excellence and dynamic capabilities.

The purpose of this review is to evaluate existing learning agility measures and offer recommendations for their use in organizational and scholarly contexts.

This is a general review paper assessing the psychometric qualities of prevalent learning agility measures. Measures were selected based on their predominance and use in the learning agility literature and organizational settings.

Learning agility measurement is an area requiring further research. Multiple conceptualizations of learning agility exist, making the true structure of learning agility unclear. The learning agility measures in the academic literature deviate from learning agility’s traditional conceptualization and require further validation and convergent validity studies. Commercial measures of learning agility exist, but their development procedures are not subjected to peer review and are not widely used in academic research, given the cost associated with their use.

Learning agility is prevalently used in organizational settings and is receiving increased scholarly attention. Various conceptualizations and measurement tools exist, and it is unclear how these theories and measures relate and differ. This paper contributes to practice by providing practical guidelines and limitations for measuring learning agility.

Learning agility was initially conceived as a multidimensional construct comprising people agility, results agility, change agility and mental agility. As the construct has evolved, the dimension structure of the measure has evolved as well. This study addresses a gap in our current understanding of how to conceptualize and measure learning agility.

Information technology (IT) personnels’ technical, business and behavioral skills are critical enablers for generating IT value. In an increasingly digitalized working environment where non-IT employees participate in digital innovations, a focus on IT personnels’ skills only doesn’t meet researchers’ need for a framework to study digital skills and managers’ need to address digital skills challenges across an enterprise’s workforce. Nevertheless, the digital skills topic is complicated by conceptual ambiguity and a lack of theoretically derived and empirically validated model. The purpose of this study is to address this problem.

Theoretically, this study draws on human capital (HC) and resource-based view (RBV) theories. Empirically, it follows mixed method combining interviews and a survey.

The digital skills construct is a multidimensional second order reflective construct. While its development is influenced by an organization’s commitment and exposure to digitalization, it influences the value organizations obtain from digitalization.

This study conceptualizes the digital skills construct, identifying technology agnostic subdimensions that are meaningful beyond a particular digital domain [information and communication technology (ICT), information, Internet, Inter of Things (IoT)] and establishing a valid measure. Other researchers can improve both the indicators of the existing four conceptually distinct and managerially recognizable workplace digital skills dimensions as well as testing new ones.

Managers can use the instrument to assess the extent to which their non-IT workforces are equipped with digital skills and get strategic insights for specific interventions such as upskilling or buying in skills.

The main theoretical contribution of the paper is the conceptualization and validation of the digital skills construct for the non-IT workforce. Furthermore, we provide a theoretical framework to explain the factors that could influence the development of digital skills and demonstrate the impact that digital skills have on selected digitalization value indicators. This contribution provides the foundation for investigating the drivers, outcomes and the relationship of digital skills to other constructs such as digital transformation, innovation and firm performance.

An overview of knowledge elicitation, representation and prioritisation of key performance indicators (CSFs) necessary for success in direct selling in a dynamic market on a woman‐to‐woman basis is presented. The CSFs were elicited using the Repertory Grid (RepGrid) method. The RepGrid method allows for construct elicitation and the exploration of constructing indices, such as, frame differentiation, frame complexity, frame integration and construct centrality. The CSF constructs are then prioritised using the Analytic Hierarchic Process. The proposed two‐stage methodology offers the benefit of eliciting individual mental constructs about critical knowledge, the representation of those constructs in a meaningful form, and the representation of repositories of knowledge of multiple entities across and within organisations.

The objective of this chapter is to provide strategy researchers with a general resource for applying structural equation modeling (SEM) in their research. This objective is important for strategy researchers because of their increased use of SEM, the availability of advanced SEM approaches relevant for their substantive interests, and the fact that important technical work on SEM techniques often appear in outlets that may not be not readily accessible. This chapter begins with a presentation of the basics of SEM techniques, followed by a review of recent applications of SEM in strategic management research. We next provide an overview of five types of advanced applications of structural equation modeling and describe how they can be applied to strategic management topics. In a fourth section we discuss technical developments related to model evaluation, mediation, and data requirements. Finally, a summary of recommendations for strategic management researchers using SEM is also provided.

Organizational researchers have become increasingly interested in multi-level constructs – that is, constructs that are meaningful at multiple levels of analysis. However, despite the plethora of theoretical and empirical work on multi-level topics, explicit frameworks for validation of multi-level constructs have yet to be fully developed. Moreover, available principles for conducting construct validation assume that the construct resides at a single level of analysis. We propose a five-step framework for conceptualizing and testing multi-level constructs by integrating principles of construct validation with recent advancements in multi-level theory, research, and methodology. The utility of the framework is illustrated using theoretical and empirical examples.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

The purpose is to assess current construct measurement in social entrepreneurship and provide recommendations for future construct measurement on the topic.

We use content analysis to assess the construct measurement practices in social entrepreneurship research. Prior studies were coded and analyzed to assess the way social entrepreneurship researchers have developed measures for key constructs in the social entrepreneurship literature. The content analysis allows for the examination of the number, type, and measures associated with social entrepreneurship research and for the comparison with the construct measurement practices in entrepreneurship research, in general.

We suggest that, while initial quantitative research has provided a useful start for empirical analysis of social entrepreneurship, future research can be improved by developing and applying stronger measures of key constructs, such as social value, mission consistency, and performance of social enterprises.

This chapter takes a content analytic approach to provide evidence regarding how a foundational element such as construct measurement has developed within social entrepreneurship research. We also propose directions for improving future research by validating and strengthening measurements of core constructs in social entrepreneurship.

With human resource (HR) roles evolving to encompass wider responsibilities, HR decision-making in organizations has become more complex than ever. This has compelled researchers in the area to move beyond simplistic models to testing models that involve studying the relationship between multiple independent and dependent variables in the presence of moderators and mediators, in order to make relevant contribution to managerial decision-making. Thus, research in the field is heavily dependent on multivariate techniques that can run several regressions simultaneously and can study the influence of one variable on the other, in presence of the other variables in the model. Structural equation modeling is the most widely used multivariate technique and involves two phases – measurement model to test reliability and validity of study constructs and structural model that involves path diagrams to test the causal relationships between these constructs. At times, however, the researcher might run into trouble with validity issues of constructs in the measurement model; especially when dimensions of a larger construct are used as independent constructs in the study. Introducing a second-order construct in such a case could be the solution to proceed further. Using empirical data, this chater illustrates the case of such a problematic measurement model and details the research methodology of introducing and working with a second-order construct in a step-wise manner, starting with an exploratory factor analysis and subsequently, moving toward a confirmatory factor analysis, highlighting the best practices to be followed while using these statistical techniques.

This chapter draws from an understanding of measurement error to address practical issues that arise in measurement and research design in the day-to-day conduct of research. The topics include constructs and measurement error, the measure development process, and the indicators of measurement error. The discussion covers types of measurement error, types of measures, and common scenarios in conducting research, linking measurement to research design.