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This study aims to explore how not-for-profit organisations (NFPs) adopt service innovation and improve their employee resilience capabilities as a response to environmental changes arising from marketisation of public services.

Using a multiple case-study research design, this study involved 32 interviews with frontline employees working in a not-for-profit care-providing organisation.

This study finds that the development of absorptive capacity can facilitate service innovation adoption in NFPs and improve employee resilience in times of transition.

This study offers theoretical insights on service innovation, absorptive capacity and employee resilience in NFPs. It makes practical recommendations that will enable NFPs to help frontline employees better adopt service innovation practices in business models endorsed by the private sector.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

National surveys reveal that sports fans exhibit greater support for athletes, sports teams and leagues endorsing social justice initiatives compared to the general population, highlighting the potential of sports for positive social impact. This study investigates whether such responses are influenced by systematic biases.

Replicating a Nielsen national survey, two experiments explore whether biases affect support for athletes' participation in the Black Lives Matter (BLM) movement. The study also examines partisan motivated reasoning as a factor driving sports fans' support for BLM.

While avid fans display stronger endorsement of BLM compared to causal/non-sports fans, evidence suggests that systematic biases distort these responses. When sport identity becomes salient, reported support for the BLM movement becomes inflated.

Researchers often employ self-report surveys to gauge audience perceptions of athlete activism or cause-related initiatives, particularly when assessing their impact. This study's findings indicate that this context is susceptible to SDB.

The study underscores the role of systematic biases in self-report surveys, particularly in socially desirable contexts. People tend to over-report “positive behavior,” leading survey participants to respond more favorably to questions that are socially desirable. Therefore, interpreting survey results with caution becomes essential when the research context is deemed socially (un)desirable. It is crucial for researchers to apply appropriate measures to identify and mitigate systematic response biases. The authors recommend that researchers adopt both procedural and statistical remedies to detect and reduce social desirability biases.

Considering that corporate social responsibility (CSR) addresses a wide range of claims from multiple stakeholders, companies must determine their CSR scope. This paper aims to examine what factors influence a firm’s decision in its scope of CSR. In exploring what factors influence CSR scope, the authors examine the relationship between a firm’s prosocial orientation and CSR and further examine its boundary conditions by the existence of CSR department.

This study uses a data set – the Social Value Survey – administered by the Center for Social Value Enhancement Studies based in the context of Korean firms. Based on 86 firm responses, statistical models were performed to test hypotheses.

The findings show that a firm’s prosocial orientation is positively associated with CSR scope. Further, this study shows that there is a negative moderating effect of the CSR department for the relationship between the prosocial orientation and CSR scope.

This study attempts to contribute to the extensive line of work on the antecedents of CSR by exploring the simultaneous existence of various drivers of CSR and the interplay between the drivers. And this study enhances the understanding on what factors influence the decision of CSR scope within a complex system of diverse stakeholder relationships. Additionally, this study has potentially shed light on the role of CSR departments to determine CSR scope.

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

Amidst unpredictable and turbulent periods, such as the COVID-19 pandemic, service organization’s responses are required to be innovative, adaptable and resilient. The purpose of this study is to explore the utilization of both reconfiguration and transformational strategies as instruments for cultivating resilience and advancing sustainability in service organizations.

The study examines a proposed resilience model using fuzzy logic. The research also used a semantic differential scale to capture nuanced and intricate attitudes. Finally, to augment the validity of the resilience model, a measurement scale was formulated using business mathematics and expert opinions.

Although investing in resilience training can help organizations gain control and maintain their operations in times of crisis, it may not directly help service organizations understand the external turmoil, seek available resources or create adaptive remedies. Conversely, high levels of reconfiguration and transformation management vigour empower a service organization’s revolutionary, malleable vision, organizational structure and decision-making processes, welcoming talented and innovative employees to enhance capabilities during crises.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations identifying the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research guides service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. The study elaborates on the enhancement of resilience, increasing innovation, improving efficiency and enhancing customer satisfaction for service organizations to remain competitive and contribute to positive social and economic outcomes through the adoption of both reconfiguration and transformational strategies.

The study also guides the service organizations to become more resilient to external shocks and adapt to changing circumstances by diversifying their offerings, optimizing their resources and adopting flexible work arrangements. Rapid innovation and business model innovation are essential components, enabling service organizations to foster a culture of innovation and remain competitive. In addition, the adoption can lead to improved financial performance, job creation and economic growth, contributing to positive social and economic impacts.

The resilience model bestows a comprehensive understanding of the pertinence of building resilience for service organizations. It identifies the antecedents that influence the adoption of these strategies and introduces a range of theoretical perspectives that empowers service organizations to conceptualize and plan for building resilience. The research also provides a foundation for further investigation into the effectiveness of these strategies and their impact on organizational performance and sustainability. By better preparing service organizations for disruptions and uncertainties, this research triggers ameliorated organizational performance and sustainability.

Within the realm of the service industry, the present investigation has undertaken the development, quantification and scrutiny of both resilience and tenacity. In addition, it has delved into the intricate dynamics surrounding the influencing factors and antecedents that bear upon resilience, elucidating their consequential impact on the operational performance and outlook of service-oriented organizations. The findings derived from this research furnish valuable insights germane to enhancing operational efficacy and surmounting impediments within the sector.

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.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Firms can boost performance by designing and implementing effective training programs for managers and employees. To achieve desired outcomes, focus should be placed on improving knowledge and skills that can lead to performance enhancement.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.