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The purpose of this paper is to examine the interdependent relationship between knowledge management and the academic performance of faculty members in higher educational institutions (HEIs) in Nepal.

This study applied survey research to identify the interdependencies between knowledge management and academic performance in the context of HEIs. The data were collected from the 445-academic staff of four different universities of Nepal by using the self-constructed questionnaire, using Delphi methods. Factor analysis was applied to identify the dimensions of knowledge management and academic performance and canonical correlation analysis was applied to examine the interdependent relationship between dimensions of knowledge management and academic performance.

The factor analysis explored the following seven dimensions of knowledge management: knowledge utilization, acquisition, generation, dissemination, transfer, creation and presentation and four dimensions of academic performance as research and publication, innovation, interactive learning and capacity building. The analysis of canonical correlation showed the interdependent relationship between knowledge utilization, acquisition, generation and dissemination with research, publication and capacity building; knowledge creation with innovation; and knowledge transfer and presentation with interactive learning.

This study was carried out based on the day-to-day practices and perceptions of the faculty members of four different universities in Nepal. The changing context of global practices in academia, organizational structure and thoughts of faculty members are changing rapidly. It demands the practical aspects of transferring tacit knowledge to explicit one to enhance intellectual capital of individual.

In a country such as Nepal, the concept of knowledge management is in an emerging stage and has been applied to some financial institutions so far. In this context, this paper presents a study that was carried out to explore the interdependence of knowledge management practices in HEIs to enhance academic activities and discourses. The knowledge management further strengthen the intellectual capital of individual and institution and impacts on overall development of knowledge economy of nation.

The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.

By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.

Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).

Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.

To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.

The present study aims to examine whether leisure time posts shared on WhatsApp status drive to travel and tourism consumption among users.

In this study, discriminant analysis was employed to test hypotheses and identify essential factors.

The study indicated that the eight most contributing factors are expressing happiness, planning leisure time, views and comments, attractiveness, inquiring about places, preferring to post, nice way of expression and relax. These factors came from the latent variables of attitude, motivation and self-expression. Overall, the main influencing factors are internal (attitude and motivation), followed by an external factor i.e. self-expression. Additionally, the findings indicated that these significantly and positively impact travel and tourism consumption.

The discriminators identified in the study would guide tour and travel agencies and the agencies' managers on how best to adopt WhatsApp and WhatsApp's status application to influence aspiring travelers.

This study enlarges the existing literature by integrating three factors, attitude, motivation and self-expression, into a model to influence the behavioral outcomes of aspirational travelers using WhatsApp status.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

This research aims to develop and test a model for measuring end-user satisfaction (EUS) in a practical manner and then statistically verify its reliability and validity.

A comprehensive list of attributes using extensive literature review, focus group and brainstorming meetings were used to create a set of attributes for the model. These attributes were then used in a survey among the end-users (N = 687) of seven case studies with different characteristics (type, size and location) to assess the reliability and validity of the model. The statistical methods included reliability tests (Cronbach's alpha), confirmatory factor analysis (CFA) and correlation analysis), canonical discriminant analysis (CDA), item response theory (IRT) and model specification tests.

EUS must be quantified before determining a project's overall performance. The analysis of repeatability and consistency (reliability and validity) performed on case studies (data collected from the end-users) strongly suggests that the EUS model is robust for a range of project types.

Although many studies have proposed customer satisfaction models in the project success context, research on quantitative measurement tools is scarce. The paper departs from past research and develops and validates a new EUS model independent of project characteristics (while the study's limitations are acknowledged).

Topology optimization of structures under self-weight loading is a challenging problem which has received increasing attention in the past years. The use of standard formulations based on compliance minimization under volume constraint suffers from numerous difficulties for self-weight dominant scenarios, such as non-monotonic behaviour of the compliance, possible unconstrained character of the optimum and parasitic effects for low densities in density-based approaches. This paper aims to propose an alternative approach for dealing with topology design optimization of structures into three spatial dimensions subject to self-weight loading.

In order to overcome the above first two issues, a regularized formulation of the classical compliance minimization problem under volume constraint is adopted, which enjoys two important features: (a) it allows for imposing any feasible volume constraint and (b) the standard (original) formulation is recovered once the regularizing parameter vanishes. The resulting topology optimization problem is solved with the help of the topological derivative method, which naturally overcomes the above last issue since no intermediate densities (grey-scale) approach is necessary.

A novel and simple approach for dealing with topology design optimization of structures into three spatial dimensions subject to self-weight loading is proposed. A set of benchmark examples is presented, showing not only the effectiveness of the proposed approach but also highlighting the role of the self-weight loading in the final design, which are: (1) a bridge structure is subject to pure self-weight loading; (2) a truss-like structure is submitted to an external horizontal force (free of self-weight loading) and also to the combination of self-weight and the external horizontal loading; and (3) a tower structure is under dominant self-weight loading.

An alternative regularized formulation of the compliance minimization problem that naturally overcomes the difficulties of dealing with self-weight dominant scenarios; a rigorous derivation of the associated topological derivative; computational aspects of a simple FreeFEM implementation; and three-dimensional numerical benchmarks of bridge, truss-like and tower structures.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

Over the past 20 years, partial least squares (PLS) has become a popular method in marketing research. At the same time, several methodological studies have demonstrated problems with the technique but have had little impact on its use in marketing research practice. This study aims to present some of these criticisms in a reader-friendly way for non-methodologists.

Key critiques of PLS are summarized and demonstrated using existing data sets in easily replicated ways. Recommendations are made for assessing whether PLS is a useful method for a given research problem.

PLS is fundamentally just a way of constructing scale scores for regression. PLS provides no clear benefits for marketing researchers and has disadvantages that are features of the original design and cannot be solved within the PLS framework itself. Unweighted sums of item scores provide a more robust way of creating scale scores.

The findings strongly suggest that researchers abandon the use of PLS in typical marketing studies.

This paper provides concrete examples and techniques to practicing marketing and social science researchers regarding how to incorporate composites into their work, and how to make decisions regarding such.

This work presents a novel perspective on PLS critiques by showing how researchers can use their own data to assess whether PLS (or another composite method) can provide any advantage over simple sum scores. A composite equivalence index is introduced for this purpose.

The purpose of this study is to dissect the transport infrastructure performance, public spending in transport infrastructure development and the manufacturing sector in determining the transport sector energy consumption.

An analysis of transport energy consumption with the transport infrastructure performance, public spending in transport infrastructure and manufacturing sector output in India using annual data for the period 1987–2019. The study used the autoregressive distributed lag (ARDL) bounds test approach along with FMOLS, DOLS and canonical cointegration regression (CCR) methods.

The results of the ARDL bounds test provide evidence for the long- and short-run relationships among study variables. It evidenced that transport infrastructure performance reduces transport energy consumption by using FMOLS, DOLS and CCR methods. Furthermore, the inference of the positive impact of value added in the manufacturing sector on transport energy consumption validates the higher energy demand of the manufacturing sector from a mobility perspective.

The estimated finding of this study is expected to be contributing to policy-making discussions on transport infrastructure and manufacturing sector development in an emerging economy like India with insights on energy consumption.

To the best of the authors’ knowledge, this is the first study that integrates the impact of manufacturing sector output on transport sector energy consumption along with transport infrastructure performance and public investment in the transport infrastructure.

The purpose of this paper is to introduce a novel cross (+) type yoke with hybrid electromagnets and new reluctance modeling to precisely calculate attraction force is given.

The comparison of attraction force and torque analyses between the proposed formulation and the existing formulation in the literature is comparatively presented. For the correctness of the force and torque values calculated in the model created, the system was created in ANSYS Maxwell and its accuracy was proved by making analyses. The maglev carrier system is inherently unstable from the point of view of control engineering. For that, it needs an active controller to eliminate this instability. For the levitation of the carrier system, it is necessary to design a controller in three axes (z, α and β). I-PD controller was designed for the air gap control of the carrier system in three axes and the controller parameters were determined by the canonical method.

While the new formulation proposed in the modeling of the carrier system has a maximum error of 1.03%, the existing formula in the literature has an error of 16.83% in the levitation distance point.

A novel cross-type hybrid carrier system has been proposed in the literature. With the double integral used in modeling the system, it takes a long time to solve symbolically, and it is difficult to simulate dynamic behavior in control validation. To solve this problem, attraction force and inclination torque values are easily characterized by new formulation and besides the simulations are conducted easily. The experimental setup was manufactured and assembled, and the carrier system was successfully levitated, and reference tracking was performed without overshoot.