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The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

According to extensive analysis, employee agility is influenced by teamwork, coordination and the organizational environment. However, less consideration has been given to the role of work stressors (challenge, hindrance) in influencing employee agility. To address this research gap, this study sheds light on how the use of enterprise social media (ESM) for social and work purposes influences employee agility through work stressors.

This research also explores how ESM visibility enhances the interaction between work stressors and employee agility by using primary data obtained from Chinese workers. A total of 377 entries were analyzed using AMOS 24.10 tools. All the hypotheses were tested using structural equation modeling (SEM).

The findings revealed that ESM use (social and work) negatively impacts challenge and hindrance work stressors. The results also reflect that challenge stressors have a significant impact on employee agility, whereas hindrance stressors are negatively related to it. Furthermore, the outcome also indicated that increased ESM visibility reinforces the connection between challenge stressors and employee agility. However, ESM visibility did not indicate a significant moderating impact on the link between hindrance stressors and employee agility.

This study describes how ESM usage effects agility of stressed employees. This research also explores how ESM visibility improves the interaction between work stressors and employee agility. The study results contribute to growing research on social media and employee agility and suggest several points of guidance for managers.

The purpose of this study is to extend the existing literature on knowledge management, which generally focuses on knowledge sharing. The model of this article explains how knowledge creation and team performance can be increased through the integration of social and technological factors

To empirically test the model, multi-wave and multi-source data were collected from 80 teams whose members use social media as a tool for communication and interaction.

The analysis results provide insights into some interesting findings. The results show transactive memory system (TMS) as an important factor that can significantly contribute to knowledge creation in teams. Especially, the TMS strengthens the significant positive effect of enterprise social media (ESM) and insignificant positive effect of knowledge complementarity on knowledge creation. Furthermore, knowledge creation is found to be a significant predictor of team performance

Much of the knowledge management literature focuses on the ways to increase the quantity of accessible knowledge to organization members. Such knowledge management studies are more relevant to knowledge exchange among individual employees, teams and organizations. However, this study takes a nuanced approach to explore how knowledge creation can be increased in teams by implementing a knowledge integration mechanism. A general model of knowledge creation is proposed, but the strength of this model lies in the moderating effect of TMS which strengthens the effect of knowledge complementarity and ESM on knowledge creation in teams which eventually increases team performance.

This study aims to investigate the impact of enterprise social media (ESM) usage on employee agility through communication quality and visibility. Theory of communication visibility is used to explore the moderating role of ESM-related strain in the relationship among communication quality, visibility and employee agility.

The research model is analyzed using data from 240 Chinese employees of different companies. The proposed hypotheses are tested using structural equation modeling, hierarchical regression and mediation analysis.

Findings suggest that ESM usage is positively related to communication quality and visibility. Communication visibility mediates the relationship between ESM usage and employee agility. ESM-related strain reduces the relationship between communication visibility and employee agility. However, ESM-related strain has an insignificant moderating effect on the relationship between communication quality and employee agility.

The present study examines how employee agility is associated with ESM usage in the workplace. The mediating effects of communication quality and visibility are explored using a theoretical foundation. This study provides empirical evidence on how the impact of ESM on employee agility is transmitted through communication visibility. Lastly, the current study quantifies the advantages of ESM for organizations and may motivate managers to confidently deploy ESM in the workplace.

The findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures.

We have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies.

BA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities.

There are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.

The study has two aims: first, it aimed to investigate the impact of contextual factors (such as information quality, service quality, system quality, trust in applications (app) and COVID-19 health anxiety) on the intention to use the Mobile Payment (MP) app, and subsequently, the actual use of the app. Second, the aim of this study is whether the COVID-19 threat has a moderating influence on the relationship between customers' intent to use MP app and the actual use of MP app.

The data are collected through an online survey from 341 Mobile Banking (MB) app users from Pakistan to empirically analyze the relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety and COVID-19 threat, intentions to use MB-app and actual use of MB-app.

The empirical analysis of the data collected from MB-app users from Pakistan shows that service quality, system quality, information quality, trust in the app and COVID-19 health anxiety positively related to intentions to use MB-app, consequently affect the actual use of MB-app. Furthermore, the results demonstrate that the COVID-19 threat positively moderates the relationship between intentions to use MB-app and actual use of MB-app.

Although, prior research established a positive impact of mobile apps on customer service and consumer satisfaction. Yet, it is not clear which factors influence customers to adopt MB-app. This study contributes to the research on MB-apps based on adaptive structuration theory and examines the technological factors and contextual factors that collectively explain when and how individuals decide to adopt MB-app.

Cryptocurrencies, such as Bitcoin, generate innovative and fast exchanges without any physical form and facilitate online payments; thus, they may bring about revolutions of the future economic system. Recent investigations reveal that China, the second largest Bitcoin market, accounts for a huge volume of Bitcoin trading and mining, which can cast distinct influences on future values of Bitcoin. Therefore, it would be of great significance to probe into the repurchase intention of Bitcoin of the Chinese individuals.

One hundred and forty-three questionnaires were collected from Chinese respondents. SPPS was employed for data analysis of the proposed hypotheses.

The results show that expectation has a positive impact on perceived enjoyment and perceived ease of use. A positive relationship between perceived ease of use and perceived enjoyment was confirmed. The findings also reveal that expectation, perceived enjoyment and perceived ease of use have significant impact on satisfaction. Moreover, it is found that perceived enjoyment, perceived ease of use and satisfaction significantly influence the repurchase intention of Bitcoin.

This study encourages future comparative studies to be conducted. Besides, it is recommended to find out other possible antecedents of repurchase intention. Moreover, this study suggested negative effects of Bitcoin to be explored.

In a practical standpoint, this study provides valuable suggestions about cryptocurrencies use and regularization. For instance, education and learnability issues of novice users need to be considered. Further, the regularization/implementation of cryptocurrencies/blockchain technologies is also suggested.

This is the pioneer endeavor which investigates the repurchase intention of Bitcoin. The findings explore some of the possible antecedents which influence repurchase intention of Bitcoin. These findings provide valuable insights and enrich the existing body of literature in the domain of Bitcoin.

This study aims to examine process improvement strategy (PIS) (proxied by remote work, workforce training, and technological innovation), and employee productivity amid the COVID-19 pandemic among bank employees.

The study employed cross-sectional and descriptive design by applying multistage sampling techniques using convenience sampling to select the study organization and stratified and simple random sampling to select 900 respondents for the study. Data were collected by using validated measures of the study variables designed into a questionnaire. Pearson’s correlation and simple regression analysis were employed to establish relationships and causal effects among variables respectively.

Results showed significant relationships between the PIS (work-from-home, workplace training, and technological innovation) and the outcome variable (employee productivity); and predictive capabilities between the PIS and the outcome variables (employee productivity). The study revealed that remote work accounted for the highest variability (R2 = 0.775) in employee productivity, followed by workplace training (R2 = 0.499), and finally investment in technological innovation (R2 = 0.486)] and as such PIS fosters employee productivity and may, therefore, be applied when faced with a similar pandemic in the future.

The study was recognized for its significance in examining how PIS supports enhancing employee productivity in banks and, by extension, other organizations during a pandemic. The research has proven to be crucial in providing insights into bank management in emerging economies and other organizations worldwide that have previously gone unnoticed during a pandemic. It has aided in the extension of existing literature on PIS and employee productivity by carefully developing a framework, thus covering practical knowledge gaps.

Snack industry is one of the fastest growing food sectors globally, and people are nowadays conscious about intake of healthy snacks on regular basis. There is enormous variety of ready-to-snacks available in the market. Brown rice though highly nutritious in comparison to polished rice is consumed meagerly by masses. Each raw material/ingredient used in extrusion cooking requires specific control of processing variables to meet acceptable product characteristics and consumer demands, which in turn necessitates the need to optimize the conditions for development of brown-rice-based snacks. The aim of this study was to optimize the extrusion cooking conditions for development of brown-rice-based extrudates.

Extrusion conditions were optimized through design expert using central composite rotatable design (CCRD) experimental design. The effect of feed moisture (10–22%), screw speed (215–385 rpm) and barrel temperature (95–160 °C) on specific mechanical energy (SME), bulk density (BD), water absorption index (WAI), water solubility index (WSI), expansion ratio (ER), breaking strength (BS) and instrumental color (L*, a*, b*) was evaluated.

All the system and product responses were significantly (p < 0.01) affected by independent variables. Regression models obtained were highly significant with high coefficient of determination (R² = 0.992). The optimum extrusion conditions obtained by numerical optimization for development of snacks were moisture content of 12%, screw speed of 350 rpm and temperature of 133 °C. The vitamin B₁ content of brown-rice-based snacks was 0.45 mg/100 (50% of RDA) whereas no vitamin B₁ was detected in white-rice-based snacks used as control.

The developed snacks contain 0.45 mg/100 g of vitamin B₁. If a person on an average consumes 150 g of snacks in a day, 50% of RDA (1.2 mg/day) for vitamin B₁ can be sufficed. Therefore, developed snacks can prove to be a viable vehicle to reduce the vitamin B₁ deficiency burden among the target population. Large-scale production and consumption of such type of snacks could improve the nutritional status of vitamin B1 deficient people. Furthermore, it can also provide a good opportunity for snack industry to develop nutritious snacks through utilization of brown rice.

Brown rice flour contains nutrients such as iron, calcium, zinc, sodium and vitamin B₁ in appreciable portions and was thus explored for development of nutritious snacks. Moreover, developed snacks recorded an overall acceptability of 4.70 out of 5, which depicts it is acceptable for mass production and consumption.

Hospital activities restore health and save lives; yet they produce infectious waste that might harm people and the environment if not handled carefully. The sudden increase in bio-medical waste (BMW) generation was caused due to the outbreak of Coronavirus disease (COVID-19) at the beginning of year 2020. There is a huge potential of businesses from BMW generated in the developing economies. The document destruction process would prove to be beneficial in determining the strategies and policies for the business from BMW. Therefore, the purpose of this study was to identify and evaluate the factors that influence the business prospects from BMW in developing economies.

The study focuses on identifying 18 factors based on an extensive literature review, questionnaire, theoretical foundations and interviews with experts in the context of developing economies. These factors are categorized into four groups namely “Culture, education and microeconomics”, “Infrastructure, social provision, and technology”, “Policy and institution” and “Integrated waste management system”. Consequently, these factors are prioritized to assist the decision-maker in comprehending the most critical aspects of accomplishing the best business practices. A framework based on the Best–Worst Method (BWM) is applied to evaluate and determine the significance of each identified factor.

The major contribution of this paper is to identify key factors that are responsible for successful implementation of the business practices from the generated BMW. The study also explains their nature, significance and inter-relationship among them using BWM. Current study applied a distinctive modeling approach to examine the contextual relationship among the key factors and to rank them as per their importance. The findings of the study suggest that “Setup for collection and segregation is the best alternative for business from BMW produced”.

The paper also presents strategies, which can be adopted as a guiding step by the decision-makers to formulate policies for the effective business practice from BMW. This research is expected to stimulate future applications of the BWM to facilitate long-term decision-making.