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Small and medium-scale enterprises (SMEs) that operate with modest financial investments and commodities face numerous challenges to remain in business. One major philosophy used by SMEs these days is the implementation of lean manufacturing to get solutions for various issues they encounter. But is lean getting sustained over time? The purpose of this research is to design a Sustainable Lean Performance Index (SLPI) to assess the sustainability of lean systems and to pinpoint the variables that might be present as potential lean system inhibitors which hinder the sustainability of leanness.

A multi-level sustainable lean performance model is constructed and presented based on the literature research, field investigation and survey conducted by administering a questionnaire. Fuzzy logic approach is used to analyse the multi-level model.

SLPI for the SMEs is found using fuzzy logic approach. Additionally, the ranking score system is applied to categorise attributes into weak and strong categories. The performance of the current lean system is determined to be “fair” based on the Euclidean distance approach and the SLPI for SMEs.

This work is concentrated only in South India because of the country’s vast geographical area and rich and wide diversity in industrial culture of the nation. Hence, more work can be done incorporating the other parts of the country and can analyse the lean behaviour in a comparative manner.

The generalised sustainable lean model analysed using fuzzy logic identifies the inhibitors and level of performance of SMEs in South India. This can be implemented to find out the level of performance in the SMEs after a deeper study and analysis around the SMEs of the country.

The sustainable assessment of lean parameters in the SMEs of India is found to be very less in literature, and it lacks profundity. The model established in this study assesses the sustainability of the lean methodology adopted in SMEs by considering the lean and sustainability attributes along with enablers like technology, ethics, customer satisfaction and innovation with the aid of fuzzy logic.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

The aim of this paper is to investigate whether Mayors in Portugal engage in earnings management close to zero with the motivation of re-election.

The data used in this study were annual financial information from Portuguese municipalities from 2005 to 2016, as well as data on elections and Mayor re-elections involving three political cycles. The methodologies employed were quantitative, including graphical and panel data regressions.

The results indicate that municipalities used discretionary accruals to engage in earnings management to report net earnings close to zero, and re-election seems to be a motivation for earnings management behaviour. Furthermore, the results suggest that municipalities in which the Mayor is re-elected are less likely to report positive net earnings close to zero.

This paper makes a valuable contribution to the literature on earnings management in municipalities. At the theoretical level, it makes it possible to identify whether re-election is a motivation for earnings management and, in this sense, to identify patterns of behaviour by managers. On a practical level, the knowledge of a manager's behaviour patterns will help to anticipate his or her future behaviour and, consequently, may prevent inefficiencies.

Consumer product firms renowned for marketing appear to be complementing brand creation, extension and acquisition with minority equity investments in entrepreneurial brand ventures (EBVs) for strategic purposes. Similarly, EBVs are looking for growth and resources that can be accessed via inter-organizational partnerships. This flourishing industry practice and the paucity of empirical research indicates the potential for new studies. The research objective was to examine why and how large incumbents were implementing strategic brand venturing (SBV), and with this understanding to develop a framework useful for descriptive and normative purposes.

This qualitative research study comprised in-depth interviews and multiple data sources across seven case studies drawn from US subsidiaries of global firms within the consumer products industry. Grounded in resource theory, the dimensions of strategic brand equity investments are abductively derived.

The findings delineate 16 process capabilities within four aggregate clusters entailing, the designing of the SBV program, opportunity identification, brand entrepreneur partnerships and venture portfolio management. Prefaced by endogenous and exogenous antecedents, these process capabilities help to contribute strategic and financial value when implemented.

This qualitative research study yielded analytical rather than statistical generalizations. A range of market and economic factors exist in the United States contributing towards a favorable entrepreneurial and brand incubation climate. This may render the SBV concept as contingent and contextual. Furthermore, the view of brand entrepreneurs' regarding the design of the process model were not explicitly sought but inferred from the discourses of the venturing units interviewed.

The article outlines several important implementation imperatives for corporations endeavoring to competitively advantage their brand portfolios via adoption of a minority equity investing strategy in EBVs. Practitioners are cautioned against myopically adopting this process alone as a success heuristic given other factors may impact success such as changes in corporate strategy or upper echelon sponsorship.

Mission preservation for social brand ventures being tethered to a large incumbent may need to be taken into account prior to and during SBV relationships.

The research contributes to the call for greater insights into the investment processes used in venturing relationships as well as coverage of new industry sectors beyond technology industries that often characterize corporate venture capital studies. Several novel findings emerged related to the importance of—the industry ecosystem; symbiosis between the founding brand entrepreneur and brand culture; synchronization of investment strategies with an emerging brand life-cycle model and serendipitous corporate entrepreneurial opportunities.

This research seeks to reveal the mediating role of work engagement and affective commitment as individual aspects that have the potential to bridge the effect of empowering leadership on the task performance of Correctional Service counselors in Indonesia, especially due to the limited literature on these two aspects in the context of public organizations.

Quantitative research was conducted on 350 counselors throughout Indonesia. The data was collected by distributing questionnaires online. The collected data were then analyzed using Structural Equation Modeling to test the seven research hypotheses.

Empowering leadership significantly strengthens task performance, work engagement and affective commitment. For indirect effects, this study found that affective commitment partially mediates the effect of empowering leadership on task performance. Meanwhile, work engagement failed to act as a mediator because it did not significantly impact strengthening task performance.

Notably, the unexpected result of work engagement's inability to significantly boost task performance deviates from the prevailing trends observed in previous empirical research, thereby adding a novel dimension to the findings of this study.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

Professional sports teams employ highly paid managers and coaches to train players and make tactical and strategic team decisions. A large literature analyzes the impact of manager decisions on team outcomes. Empirical analysis of manager decisions requires a quantifiable proxy variable for manager decisions. Previous research focused on manager dismissals, tenure on teams, the number of substitutions made in games or the number of healthy players on rosters held out of games for rest, generally finding small positive impacts of manager decisions on team success.

The authors quantify manager decisions by developing a novel measure of game-specific coaching decisions: the Herfindahl–Hirschman Index (HHI) of playing-time across players on a team roster over the course of a season.

Evidence from two-way fixed effects regression models explaining observed variation in National Basketball Association team winning percentage over the 1999–2000 to 2018–2019 seasons show a significant association between managers’ allocation of playing time and team success. A one standard deviation change in playing-time HHI that reflects a flattened distribution of player talent is associated with between one and two additional wins per season, holding the talent of players on the team roster constant. Heterogeneity exists in the impact across teams with different player talent.

This is one of the first papers to examine playing-time concentration in the NBA. The results are important for understanding how managerial decisions about resource allocation lead to sustained competitive advantage. Linking coaching decisions to wins can help teams to better promote this core product.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The global COVID-19 pandemic has rapidly overwhelmed our societies, shocked the global economy and disturbed normal business operations. While such impacts of COVID-19 are becoming clearer, the effects of the disease on business operations are more common. This study mainly focuses on identifying the factors that affect the smooth operation of businesses during a pandemic situation.

Analytical hierarchy process (AHP) method was used to rate the result index. A total of 40 professionals and experts of different businesses were listed on stock exchanges, and asked to rank the key variables with relative indices and weighting methods.

The results of the AHP successfully assigned weighting scores to all key important factors during the COVID-19 pandemic situation that businesses should focus on, with economic factors receiving the highest score of 60%. Likewise, the other factors that impact values for business operations are reported as social (22%), legal (12.2%), technological (5.16%) and political (0.57%). The results of this study also match with the current policies adopted by different government and nongovernment agencies like the guidelines of the World Health Organization and some most recent research results.

In the hectic and growing environment under COVID-19 pandemic, more contributions are not enough, and it is helpful for the whole business industry and society by stipulating more views. This study aims to overview the global impacts and challenges of COVID-19 pandemic on business operations.