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The cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.

In total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.

The results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.

The most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.

The development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

The study identifies barriers all stakeholders face in the returns management process. The pressing issue of online product returns significantly erodes the net profit margins, demanding urgent action. Existing returns management systems are often complex, time-consuming and costly, failing to address this issue effectively. In this regard, persuasive technologies like gamification can effectively influence behaviour and enhance motivation towards reducing return by striking a balance between psychological and behavioural factors. Therefore, the study proposes a framework that combines the theory of planned behaviour and Octalysis to identify intentions to reduce product return rates in the e-commerce business.

A qualitative exploratory research design using a multiple case study-based approach with in-depth interviews conducted with 96 varied stakeholders was used to provide insights into the behavioural aspects for reducing return rates in e-commerce.

The results reveal three facets of effective returns management in e-commerce. Customers and delivery partners prioritize return processes, policies and rewards, emphasizing efficiency. Meanwhile, mid- and strategic-level employees are optimistic about return process enhancements. Interdepartmental integration and cross-functional communication are found to be vital, suggesting the need for a gamified framework in returns management.

This research enriches the returns management literature on gamification solutions and responds to the need for theoretical anchoring by applying the theory of planned behaviour at the organizational level to improve efficiency and customer centricity in the return process.

Resource mobilization and technology adoption by small firms are mainly studied separately, although considering them together is crucial for understanding how resources are accessed and mobilized to address uncertainty. Moreover, the authors know little about how small firms pursue new opportunities in a constantly changing environment. The purpose of this study is to investigate how small firms adopt technologies to engage different stakeholders and facilitate the access and mobilization of key resources in the opportunity co-creation process.

This study applied a qualitative case study method and conducted 14 interviews with co-founders or top managers of five small firms in Iran.

The findings reveal how small firms adopt technologies to access and mobilize social, human, psychological and financial resources in a highly uncertain environment to co-create new opportunities.

First, the study applies a cross-sectional approach. Therefore, it does not capture longitudinal aspects that might impact resource mobilization and technology adoption over time. Second, the selected five case firms represent rather successful firms, each of which adopted different technologies to challenge the established structure of the market. That is, this study did not focus on unsuccessful cases that would enrich the theory further.

This study reveals how small firms adopt new technologies to mobilize resources and co-create opportunities in highly uncertain environments. It reveals that small firms employ technology adoption strategies to utilize operant resources and accelerate operand resource mobilization. Active learning plays a critical role in this process.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

The primary aim of the current study is to validate the theoretical model presented by Demir (2021a, b, c) via empirical evidence. In this respect, the study intends to offer a holistic pathway for obtaining both external and internal advantages from the implementation of ISO 9001, with a particular emphasis on developing countries.

The research utilized an empirical methodology to investigate the research objective. Data were collected from various sources, including employees and managers of the various companies. In this context, a model was developed, and 170 data points were collected from businesses in the Kurdistan region of Iraq in order to test the hypotheses in this model.

After the collected data passed the reliability and validity test, the hypotheses designed were tested by applying the partial least squares method. When the results were examined, it was observed that organizational culture and organizational learning variables were the two key antecedents that these factors would emerge after applying ISO 9001 practices with discipline for a certain period of time and that organizational benefits could be obtained only as a result of them. Detailed results and suggestions were given to managers and theorists as a result of the analysis.

This study contributes to the literature as an empirical application of the theoretical work written by Demir in 2021. Demir's work, which produced a theoretical concept based on many empirical and theoretical studies done before, still needs much more empirical work in developing countries.

This study advances research on cognitive diversity from the perspective of public employees by studying the effect of cognitive diversity on team viability, the mediating effect of transactive memory systems (TMS) and the moderating effect of technology integration.

We used SmartPLS to analyze a unique data set from 193 public employees. The information was collected through an online questionnaire administered by the LimeSurvey Professional platform. In addition to analyzing the data through partial least squares structural equation modeling with higher-order latent variables, we analyzed mediating and moderating effects.

The results show that TMS act as partial mediators between cognitive diversity and team viability. Although technology integration (for both external diffusion and internal integration) moderates this relationship to mitigate negative effects, technological infrastructure does not.

This study expands previous research on TMS and technology integration. Our findings support the significance of TMS and technology integration in a context of cognitive diversity, identify ways to develop good management behavior and assess the results of these practices for team viability. We recommend that public managers in contexts of cognitive diversity work to create effective workplace environments. Training programs can foster TMS capabilities and support implementation of technology integration to improve team viability and results for public service delivery to citizens.

This study explores how the effects of youth-oriented values influence the behavioural attitudes towards online conspicuous activities during travel, leading to their further conspicuous consumption travel behaviour.

This study applied a mixed-methods approach. Semi-structured interviews were conducted with 20 young tourists to extract the youth-oriented value measurement scale. A systematic survey was conducted in a renovated heritage site in China. PLS-SEM analysis was performed on 322 responses to test the proposed hypotheses.

The results of semi-structured interviews supplement seven new items and form a measurement scale of youth-oriented values with 16 items in three dimensions (self-identification, peer-identification and eagerness to change). The results of PLS-SEM analysis reveal that all three youth-oriented values significantly influence attitudes towards information searching and content generation, and these two behavioural attitudes are positively related to continuous conspicuous consumption travel. The openness trait moderates the relationship between self-identification and content generation.

The study contributes to youth tourism research in conspicuous consumption travel behaviour. It provides insights to tourism operators to formulate strategies to develop the young tourist market in a cultural heritage tourism context.

The current study applied the concept of country image to a context of bilateral relations for two post-Soviet states to examine the impact of a mega sport event on the image of the host country Russia.

Surveys were collected before and after the World Cup to assess any changes among Georgians with regard to their views on Russia and intentions to visit.

The results showed a significant change in Russia’s pre-perceived cognitive image related to reputation, respect and development, as well as the emotional aspect and overall country image after the mega sport event. Interestingly, the affective image of Russia carried more weight in shaping the overall country image compared to the cognitive aspects, suggesting the increasing importance of emotional perceptions over beliefs. However, despite these changes, the study found that mega sport events did not significantly moderate the association between country image and behavior intentions in the context of Russo-Georgian hostile bilateral relations.

This study is one of the first examinations of the impact of hosting mega sport events in countries with hostile bilateral ties. The findings support that mega sport events can be an effective mechanism to gain soft power in that such events can arouse changes in people’s emotions and feelings towards the host country, even for those living in a country with a hostile relationship with the host country. The scope of applicability of these findings can be extended to other contexts, including future hosts of mega sport events in their geo-political contexts.

This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses.

BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding.

The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation.

The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments.

Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

The dynamic environment has necessitated searching for new ways for managing and grooming people for better performance. The purpose of this study is to explore ambidexterity in human resource management (HRM) for better management of paradoxical tensions and its effect on employee performance. Further, this research also addresses the black box in this relationship by evaluating the extraneous (managers’ ambidextrous orientation) and mediating (individual ambidexterity) variables in this relationship.

A quantitative research methodology has been used to explore the ambidexterity in HRM and its impact on employee performance. Around 470 banks have been contacted for data collection. The data have been thoroughly examined for reliability and validity. Further, it has also been checked for common method variance.

The findings revealed that individual ambidexterity mediates the relationship between ambidextrous HRM and employee performance. Further, managers’ ambidextrous orientation moderates the relationship between ambidextrous HRM and individual ambidexterity.

The present study makes an important contribution to the strategic HRM literature in general. The theoretical and practical implications have also been put forth for academic and practical fields. Lastly, the study contributes towards ambidexterity literature by examining it from an HRM perspective.