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This paper aims to investigate the feasibility of potassium permanganate (KMnO₄) as an efficient discharging agent for indigo-dyed denim fabrics and identification of key variables for its cost-efficient implication.

Response surface methodology, which is a statistical technique for the optimization of process variables, was used to study the effect of three key variables, i.e. KMnO₄ concentration, printing paste pH and reaction time on whiteness and strength of discharged printed fabric. Regression models were developed to predict response variables, i.e whiteness, tensile strength and tear strength of discharge printed denim.

It was found that some captivating discharge printing effects could be produced using appropriate KMnO₄ concentration, printing paste pH and reaction time without any significant loss in the fabric strength.

This study highlights the practical implication of KMnO₄ to be used as a safe and effective discharging agent under different conditions and to optimize the parameters using statistical analysis to ensure minimum loss in textile properties. The use of denim has evolved over the decades from a rough and tough workwear to highly fashionable apparel. Various dry and wet processing techniques have been introduced in recent years for the value-addition of denim – discharge printing is one of them. As lab to bulk reproducibility requires some sort of experience and adjustments in main parameters, the practical feasibility on the bulk scale should be adjusted in advance by means of the lab scale experimentation.

The KMnO₄ oxidation process is considered eco-friendly because manganese dioxide, which is formed when permanganate is reduced, can be recycled. Thus, the use of KMnO₄ can be considered as an eco-friendly safe process for the discharging of indigo dyes.

This study aims to investigate the drivers that motivate employees and guests' hotel service participation to understand how that can influence the implementation of Value Co-Creation (VCC) in sub-Saharan African context.

Using an interpretive paradigm, the study draws on 32 in-depth interviews, 6 focus group discussions involving 32 participants and participant observation field notes. Data were analysed using thematic analysis.

The study unravels nine motives that drive employee–guest VCC participation: passion, relationship, belongingness, shared and enhanced experiences, satisfaction, reputation development, openness, communication and rewards.

This exploratory, cross-sectional study was undertaken in hotels within sub-Saharan Africa. Thus, findings cannot be generalised. However, it provides an opportunity for future quantitative approaches within different contexts involving other stakeholders.

Considering the numerous challenges from COVID-19 pandemic on the service industry, hotel managers might want to use the findings to not only formulate policies that support employee–guest co-creation for service improvement and survival but also introduce enhanced innovative service practices that deliver on employee and guest service expectations for retention. The findings encourage hotel managers to identify employee and guest context-specific motivations to be able to match with value-driven service activities, aimed at attracting positive behaviours to better respond to the numerous COVID-19-related challenges.

This work adds to the VCC literature by investigating the collective and individual drivers at the employee and guest dyadic level within sub-Saharan African hotel context. The authors propose a comprehensive model to guide the successful implementation of employee–guest VCC.

This study aims to examine how perceived supervisor and organizational support mediate the association between talent management and work engagement in Bangladesh’s manufacturing industry. It also analyzes the association between work engagement and sustainable organizational performance.

A structured survey questionnaire was used to collect data from 385 employees working in the manufacturing industry in the Chattogram division, applying a convenience sampling approach. Data was assessed using SmartPLS software.

The key findings are that perceived supervisor and organizational support mediate the relationship between talent management practices and work engagement. Moreover, work engagement positively influences an organization’s sustainable performance.

This study adds knowledge to the existing literature on talent management, work engagement and sustainable organizational performance. The findings may improve employee work engagement through talent management practices in Bangladesh’s manufacturing industry.

This study is one of the few in the Bangladeshi manufacturing industry that provides an extensive overview of talent management practices and work engagement, using perceived supervisor and organizational support as mediating variables.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

This study aims to develop a neurocomputational approach using the Levenberg–Marquardt artificial neural network (LM-ANN) to analyze flow and heat transfer characteristics in mixed convection involving radiative magnetohydrodynamic hybrid nanofluids. The focus is on the influence of morphological nanolayers at the fluid–nanoparticle interface, which significantly impacts coupled heat and mass transfer processes.

This research simplifies a complex system of higher-order nonlinear coupled partial differential equations governing the flow between orthogonal coaxially porous disks into ordinary differential equations via similarity transformations. These equations are solved using the shooting method, and parametric studies are conducted to observe the impact of varying important parameters. The resulting data sets are used to train, validate and test the LM-ANN model, which ensures high predictive accuracy. Machine learning and curve-fitting techniques further enhance the model’s capability to generate detailed visualizations.

The findings of this study indicate that increased nanolayer thickness (0.4–1.6) significantly improves thermal performance, while changes in the chemical reaction parameter (0.2–1) have a notable effect on enhancing the Sherwood number. These results highlight the critical role of morphological nanolayers in optimizing thermal and mass transfer efficiency in MHD nanofluids.

This research provides a novel neurocomputational framework for understanding the thermal and mass transfer dynamics in MHD nanofluids by incorporating the effects of interfacial nanolayers, an aspect often overlooked in conventional studies. The use of LM-ANN trained on computational data sets enables high-fidelity predictive analysis, offering new insights into the enhancement of thermal and mass transfer efficiency in hybrid nanofluid systems.

Many tourism academics have investigated the linkages between tourism, power and space; few have specifically addressed the profound links between tourism and geopolitics. In view of the restrictive assumptions of the linear framework used in the earlier studies, and hidden asymmetries present in the time series data. Against this backdrop, the study tries to find out how tourists may respond differently to favourable and unfavourable shocks in geopolitical risk (GPR).

In order to capture this asymmetric nature of the problem, the study employs the non-linear autoregressive distributed lag (NARDL) model to evaluate data from 2001Q1 to 2019Q4.

The results show that both positive and negative shock to GPR does not produce results of equal magnitude. A positive shock to GPR has a more detrimental effect on foreign tourist arrivals (FTA) than a beneficial effect a negative shock produces. Besides this, the present study also looks at the effect of other macro-economic variables on FTA. An ascend in the real effective exchange rate (REER) i.e. appreciation of the domestic currency has an unfavourable impact on foreign visitor arrivals, while an increase in world gross domestic product amplify it. The results of the study are robust to alternative measures of the control variable.

The study is significant for policymakers in understanding the short and long-run implications of GPR on FTA in India. The present study can assist policymakers, and destination managers to manage the external and internal risks and minimise the consequences of geopolitical threats on the Indian tourism industry. Consequently, destination managers can utilise the study's findings in calibrating their operations and designing crisis marketing strategies within the geopolitical dynamics of the Indian state.

The study tries to find out how tourists may exhibit distinct reactions to positive and negative disturbances in GPR. The study provides first-hand evidence of how GPR impacts tourism demand. The paper also includes the existing body of literature related to GPR factors and their effect on tourist influx, specifically in the framework of the Indian tourism sector.

The world we are living today is faced with social challenges that call for radical innovation. The rapid pace of technological developments played a key role in delivering social developments in a cautious manner that will ensure a promising future for social capital. Social innovation is a phenomenon that is put forward to urge countries to find solution to address the recurring problems of social inequalities that were not able to be eliminated by the existing rules and policies. It involves understanding the complexity of the society ecosystem and task calls for a partnership and networks between the nonprofit, public, and private sectors. This chapter explains how Malaysia utilizes public–private partnership to promote social innovations in the country. Through the 11th Malaysia Plan, Malaysia envisages to put forward the well-being and development of the people through social innovation to balance between the social and economic development. In order to fulfill this aim, Malaysia has implemented various programs and policies through the social innovation ecosystem that has been formed. The ecosystem is comprised of four initiatives, which includes microfinancing; social entrepreneurship; public service delivery; and grassroots innovation of science and technology. These initiatives will be collaboratively developed using the concept of 4P, which is the public, private, people, and professionals. Each of this initiative is discussed thoroughly in this chapter alongside with the programs and policies that have been carried out since then. Additionally, the chapter also includes the discussion on the issues and challenges that could be seen and presumed to be the hindrance to the success of the social innovation development.

Based on the intellectual capital (IC) theory and the resource-based view (RBV), the paper seeks to elucidate the direct and indirect relationships between IC and competitive advantage in small and medium enterprises (SMEs). Therewithal, besides examining the mediating role of innovation capabilities in the IC-competitiveness link, it scrutinizes the moderating effect of entrepreneurial orientation in causing IC to boost competitiveness in SMEs through flourishing innovation capabilities.

Data were collected from 206 participants working in SMEs operating in the Kingdom of Saudi Arabia with the help of a structured questionnaire distributed through LinkedIn. Partial least square (PLS)-based structural equation modeling (SEM) using SmartPLS 4.0 has been performed to calibrate the auxiliary and structural models.

Based on the empirical analysis, IC significantly and directly enhances the competitive advantages of SMEs. Additionally, innovative capability has been found to be a complementary partial mediating condition in enabling IC to foster competitiveness in SMEs. Furthermore, the moderated mediation analysis reveals that innovation capabilities strongly mediate the association between IC and competitiveness in SMEs, characterizing higher entrepreneurial orientation.

The study provides significant insights to academicians and practitioners seeking to comprehend or configure interactions among IC, innovation capabilities, and entrepreneurial orientation in maturing competitiveness among SMEs, especially in emerging economies. Furthermore, the study provides a valuable integrative perspective on SMEs’ competitiveness by involving three voguish constituents of contemporary scholarly discourse grounded into the leading underpinning theoretical perspectives, such as IC theory, RBV, and entrepreneurship theory.

The uniqueness of this model lies in its rich theory-laden conceptualization and explanation that could extend theoretical debate and managerial action to the next levels.

This study aims to empirically examine the role of privacy, security, trust and autonomy in Millennials’ continued use of online dating applications. Emerging markets have been famous for their rapid modernization, growth and cultural shifts. Yet, consumer worries about online dating consumption need to be addressed in the literature through a better understanding of their behavioural intentions. This study examines the interplay of the underlying variables by testing a conceptual framework grounded on the theory of reasoned action (TRA), the self-determination theory (SDT) and the information systems continuance theory (ISCT) with regard to an emerging market.

A cross-sectional and quantitative research design is adopted. Using a well-designed structured questionnaire adapted from established scales, data was collected through a survey featuring 332 substantive responses from actively involved Millennial users of online dating apps in India. The data was analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM) through IBM SPSS AMOS (Analysis of Moment Structures) 24.0 to provide evidence of the reliability and validity of constructs alongside testing the hypothesis in the proposed model. Furthermore, Preacher and Hayes’ (2004) approach is used to explore mediation effects.

The results show that autonomy and trust positively affect the continued use of online dating apps. The study further reveals that autonomous behaviour motivates Millennials to use online dating apps, overriding their concerns for privacy and security. Furthermore, the results indicate that trust fully mediates the relationship between concern for privacy, perceived security and continued usage intention. Therefore, trust affects users’ intentions to continue using online dating apps as it bridges the effect of users’ concern for privacy and the perceived security in the online dating platform, making these dimensions relevant as a whole.

This study provides critical insights into the theory and practice. The findings demonstrate that autonomy is more significant in motivating Millennials to use online dating apps than concerns about privacy and perceptions of security. In addition, trust plays a crucial role in mediating the relationship between these variables. Therefore, app developers, app marketers and app stores can use the findings to motivate Millennials to continue using their platforms by encouraging a sense of freedom and a better inner self, fostering engagement and conveying user trust.

Several studies have investigated the pre-adoption behaviour of users of online dating apps, and little attention has been paid to continuance usage. This study provides a unique theoretical research model and perspective for online dating apps that uncovers the role of concern for privacy, perceived security and trust that affect post-adoption behaviour by integrating the TRA, SDT and ISCT. This integration offers a more futuristic and refined perspective on human behaviour by considering both cognitive and motivational aspects. The study establishes that autonomy outweighs concern for privacy and perceived security.

The paper examines “lessons learned” from the COVID-19 pandemic by capturing the organizational and operational experiences of police departments in the state of Washington across two waves of survey administration. This study is among the first to document experiences at a state level, affording an opportunity to compare national results and allowing for documenting shifts between each wave.

This study draws from a state-specific replication of the IACP instrument fielded by Lum et al. (2020a) on the experiences of police departments working under COVID-19 and integrates specific questions from stakeholders in Washington. The survey was administered through Qualtrics, which was distributed by the Washington Association of Sheriffs and Police Chiefs (WASPC) 6 months into the pandemic and 18-months later.

Respondents reported operational and organizational reconfigurations including shifting to telecommuting for support staff and command staff, implementing online reporting forms for use by the public to report crimes and planning exercises for predicted budget reductions. While CDC and Washington Department of Health guidelines informed most of the respondents' policy, integrating that policy into the operational procedures demonstrated how quickly issues in staffing can transition from a challenge to a severe issue. Quarantining and staffing problems remained an issue across both waves, which were exasperated by staff turnover.

The response rate and response completeness, particularly a drop in response for Wave 2, limit the ability to supply more precise estimates documenting the experiences of WA police departments. Relatedly, an inability to match Wave 1 to Wave 2 results inhabits a direct comparison of changes 12-months later. Second, the responses reflect those of the chief law enforcement officer (Chief of Police, Sheriff) or the person designated by the chief, which may not represent the experiences of front-line officers.

Reflecting on the lessons learned across each Wave, agencies shared adaptations implemented to better protect the health of staff and their communities to effectively manage future health emergencies. Most directly, they learned how to conduct some business remotely and the necessity to innovate. The most important lesson learned, and implication for policy is improving the resourcing and logistics to secure adequate health protections. The data also highlight a concern for clarity, consistency, and credibility in supplying information to police departments in pandemic and emergency situations.

This paper fulfils an identified need to enhance “lessons learned” for police response in pandemic and emergency situations. These data supply insight into the anticipated and lived experiences of agencies adapting to the pandemic in Washington State. The endogenous and exogenous shock that is COVID-19 altered how police departments interacted externally and internally resulting in operational and organizational reconfigurations.