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The purpose of this paper is to optimize the gas metal arc welding (GMAW) process input parameters simultaneously considering the multiple output variables (bead width (BW), bead height (BH) and depth of penetration (DP)).

Grey‐based Taguchi approach was used for designing the experiment, L27 orthogonal array was used which composed of three levels and 27 rows, which means that 27 experiments were carried out. Design of experiments was selected based on a four welding parameters with three levels each. The selected welding parameters for this paper are gas flow rate, voltage, travel speed and wire feed rate. The bead‐on‐plate welding trials are carried out on AISI 904L super austenitic stainless steel (SASS) sheets and evaluate the shape of the fusion zone depends upon a number of input parameters.

Bead‐on‐plate welding of 904L SASS sheet is successfully performed (without any cracks and discontinuity) by GMAW process and the bead profiles are measured. The predicted bead profiles have the better DP and lower BH and BW. It is found that the optimized setting values are improving the response values by 10 per cent.

The optimal welding conditions are identified in order to increase the productivity and minimize the total operating cost. The process input parameters effect is determined under the optimal welding combinations.

The purpose of this study is to establish a friction coefficient prediction model using texture parameters and then using the optimal texture parameters to obtain the minimum friction coefficient.

Based on texture technology and the cavitation phenomenon conditions, a test scheme based on two-factor and five-level texture parameters is designed using central composite design and then the response surface methodology and hybrid back-propagation genetic algorithm (BP-GA) models are used to establish a friction coefficient prediction model and optimize the friction coefficient.

The result indicates that the values predicted using two methodologies agree well with the experimental data, but the hybrid BP-GA model is superior to the response surface methodology model in both prediction and optimization.

Two methodologies are used to study the influence of the texture parameters on the friction coefficient under the cavitation condition. It is expected that the result can be used to obtain optimum texture parameters to reduce the friction coefficient.

The primary aim of this article is to ascertain the modalities of leveraging Lean Six Sigma (LSS) for Industry 4.0 (I4.0) with special reference to the process industries. Moreover, it intends to determine the applicability of simulation-based LSS in the automation of the mineral water industry, with special emphasis on the robust design of the control system to improve productivity and performance.

This study adopts the action research methodology, which is exploratory in nature along with the DMAIC (define-measure-analyze-improve-control) approach to systematically unearth the root causes and to develop robust solutions. The MATLAB simulation software and Minitab statistical software are effectively utilized to draw the inferences.

The root causes of critical to quality characteristic (CTQ) and variation in purity level of water are addressed through the simulation-based LSS approach. All the process parameters and noise parameters of the reverse osmosis (RO) process are optimized to reduce the errors and to improve the purity of the water. The project shows substantial improvement in the sigma rating from 1.14 to 3.88 due to data-based analysis and actions in the process. Eventually, this assists the management to realize an annual saving of 20% of its production and overhead costs. This study indicates that LSS can be applicable even in the advent of I4.0 by reinforcing the existing approach and embracing data analysis through simulation.

The limitation of this research is that the inference is drawn based on a single case study confined to process industry automation. Having said that, the methodology deployed, scientific information related to optimization, and technical base established can be generalized.

This article is the first of its kind in establishing the integration of simulation, LSS, and I4.0 with special reference to automation in the process industry. It also delineates the case study in a phase-wise manner to explore the applicability and relevance of LSS with I4.0. The study is archetype in enabling LSS to a new era, and can act as a benchmark document for academicians, researchers, and practitioners for further research and development.

This study aims to examine how chief executive officer's (CEO) personality traits influence the corporate sustainability performance (CSP) of firms. The paper also examines the moderating effect of board power on this relationship.

Using a linguistic tool (IBM's Watson Personality Insight Service), the authors measured the personality traits of 229 CEOs from 176 firms from 2009 to 2018. Firm-level CSP are obtained from the Sustainalytics database. The hypotheses are tested using multiple regression analysis. The robustness of the results of the study is confirmed by addressing endogeneity concerns and by validating the measurement of CEO personality traits using Personality Recognizer, an alternative linguistic tool.

The results show that CEO personality traits of extraversion and neuroticism are significant predictors of CSP. The paper also identifies board power as a contingent factor that influences the suggested relationships.

Using upper echelon theory and cybernetic big five theory, this paper identifies CEO personality traits as important antecedents of corporate sustainability performance and adds to the micro-foundations of corporate sustainability literature. To the authors’ understanding, this is the first study that examines the influence of CEO personality on CSP using a comprehensive trait framework. The paper also demonstrates the usefulness of text-analytic tools to measure CEO personality traits, thereby contributing to the progress of upper echelon theory.

There is an increasing trend of outsourcing maintenance activities of heavy equipment, including belt conveyor installations. However, there are numerous challenges in maintenance outsourcing. This paper aims to identify and analyze various challenges of outsourcing maintenance activities associated with belt conveyor installations.

This paper identifies maintenance outsourcing challenges of belt conveyor installations through literature review, field visits and expert opinion. An integrated structural hierarchical framework of the identified challenges is developed through analytic hierarchy process and decision-making trial and evaluation laboratory.

The paper has identified eight challenges, namely, attainment of organizational strength by contractors, legal and financial challenges for contractors, attainment of necessary technician skills by contractors, maintenance data acquisition and analysis challenges, facilitation with modern equipment, gadgets and instrumentation, service quality challenges, health, safety and environment-related challenges and spares supply chain management challenges. The segregation of driver and dependent challenges, including their hierarchical framework had been established in this work.

A comprehensive list of challenges and their prioritization in maintenance outsourcing of belt conveyor installations had been established. This will help the organizations who own and operate these installations to make judicious decisions regarding outsourcing maintenance.

This paper significantly contributes to the literature on maintenance outsourcing of heavy machinery installations like a belt conveyor system based on the input of different stakeholders. This study will lead to the development of frameworks for maintenance contractor selection for such installations.

Mergers and acquisitions (M&As) are important strategic actions undertaken by firms to access resources and markets. However, firms face substantial challenges in M&As during deal completion. While prior literature reviews synthesize the studies on the post-merger consequences of M&As, the literature on deal completion is largely fragmented. In this paper, the authors synthesize prior literature on deal completion into the antecedents and consequences framework and map various studies across the international business and management, finance and accounting literature at the macro-, meso- and micro-levels.

The authors adopt a content analysis-based methodology to conduct the review. First, the authors identify existing literature on deal completion based on keyword searches. Next, the authors propose a framework that integrates the extant literature from a multi-theoretic perspective across four broad themes: concepts, antecedents, implications and moderators. In this study, the authors consider not only empirical but also conceptual papers to strengthen the theoretical foundations of M&A literature. Finally, after synthesizing various studies, the authors highlight a future research agenda on deal completion.

Based on the review, this study provides important avenues for future research on M&A deal completion.

This study theoretically integrates multi-disciplinary and multi-country research on acquisition completion.

The purpose of this paper is to measure the academic motives of faculty in higher education to understand the pattern of relationships between work motivation scale (WMS) and technological pedagogical content knowledge (TPACK).

The research adopts regression analysis to help model the data collected from 63 faculty members from India. The empirical study investigated the relationship of intrinsic motivation (IntrM), integrated motivation (InteM), identified motivation (IdenM), introjected motivation (IntrjM), external regulation motivation (ExtR) and amotivation (Amot) with each of technological knowledge (TK), (pedagogical knowledge (PK), content knowledge (CK) and TPACK.

Optimal functioning among faculty was due to “InteM” followed by “IntrM” and “IdenM” that yielded most positive consequence (mean values>3.9). “ExtR” and “Amot” were the negative outcomes that would be counterproductive and result in employee dejection (Mean values<3.1). The results proved that all TPACK variables were positively related to “IntrM” – a self-determined motivation. Additionally, “TK” and “PK’ were positively related to “IntrjM” – a behavior that is regulated by self-esteem and self-worth; “TPACK” depicted positive relationship with “IntrjM” and negative correlation with “IdenM.”

The study supported the fact that TPACK and WMS when analyzed together can create better engagement models among the faculty for an enriching learner experience interaction. Furthermore, it would help identify specific strategies on teacher development training programs through the integration of technology.

The study is original and seeks to inspect the relationship between TPACK and WMS among faculty of higher education in Indian business schools.

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

The purpose of this paper is to develop a framework for the safety performance measurement of belt conveyor systems.

A structural methodology of graph theory and matrix approach is used for developing a framework for safety performance measurement of belt conveyor systems.

The development of a framework for safety performance measurement of belt conveyor systems is essential for ensuring plant safety. For this, safety performance factors, including design and operating contextual factors of belt conveyor systems, are identified. The factors along with their interrelations are modeled using digraph. An equivalent matrix of the digraph provided safety performance function (SPF) of belt conveyor systems, leading to the development of a safety performance index (SPI).

The developed framework will enable the designers for evaluating and comparing alternative designs of conveyor systems from the safety viewpoint. The plant operators can make inferences from the SPI to identify the weak contextual factors in the plant and develop action plans for its mitigation.

The paper is novel and employs graph theory and matrix approach for safety performance measurement. The methodology helps in the quantitative evaluation of the safety performance of belt conveyor systems.

The most critical phase of a change process is change implementation and it is evident that the masterfully originated change process fails due to its poor implementation. Therefore, the purpose of this paper is to profile how knowledge management (KM) strategies, personalization and codification, are helpful in successful change implementation by reducing employee cynicism and increasing the level of readiness for change.

Data were collected from 196 executives of National Bank of Pakistan at Time 1 (pre-implementation) and Time 2 (post-implementation) with the temporal research design. Multiple regression analysis is used to test the direct effect; Preacher and Hayes (2004) test is applied to measure the mediating effect and guidelines of Aguinis (2004) are followed for analyzing the moderating effect.

The result of the direct effect shows that both KM strategies have significant positive effect on successful change implementation. Further, mediation analysis proves that readiness for change partially mediates between KM strategies and successful change implementation. In addition, partial interactive effects of employee cynicism is observed between readiness for change and successful change implementation.

The management should initiate steps to boost personalization and codification strategies at their optimal levels. This would ultimately be helpful to implement a successful change through developing readiness for change and reducing the employee cynicism regarding change.

The area of successful change implementation in the context of KM strategies was untapped, and is examined in this study.