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This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

This study aims to focus on key approaches to education for sustainability (EfS) leadership development in the context of Malaysian and Japanese universities. The authors identify key indicators of effective EfS leadership development approaches using both descriptive and inferential analyses, identify and compare the preferred leadership learning methods of academics and examine the impact of marital status, country of residence and administrative position on the three EfS leadership development approaches.

The study is quantitative in approach and survey in design. Data were collected from 664 academics and analysed using the efficient partial least squares (PLSe2) methodology. To provide higher education researchers with more analytical insights, the authors re-estimated the models based on the maximum likelihood methodology and compared the results across the two methods.

The inferential results underscored the significance of four EfS leadership learning methods, namely, “Involvement in professional leadership groups or associations, including those concerned with EfS”, “Being involved in a formal mentoring/coaching program”, “Completing formal leadership programs provided by my institution” and “Participating in higher education leadership seminars”. Additionally, the authors noted a significant impact of country of residence on the three approaches to EfS leadership development. Furthermore, although marital status emerged as a predictor for self-managed learning and formal leadership development (with little practical relevance), administrative position did not exhibit any influence on the three approaches.

In addition to the theoretical and methodological implications drawn from the findings, the authors emphasize a number of practical implications, namely, exploring the applicability of the results to other East Asian countries, the adaptation of current higher education leadership development programmes focused on the key challenges faced by successful leaders in similar roles, and the consideration of a range of independent variables including marital status, administrative position and country of residence in the formulation of policies related to EfS leadership development.

This study represents an inaugural international comparative analysis that specifically examines EfS leadership learning methods. The investigation uses the research approach and conceptual framework used in the international Turnaround Leadership for Sustainability in Higher Education initiative and uses the PLSe2 methodology to inferentially pinpoint key learning methods and test the formulated hypotheses.

Many environmental problems are due to the unfavorable environmental intentions and cultural–behavioral weaknesses in the relationship between man and nature. This study aims to adopt an environmental psychological perspective to green intention (GI) and green behavior (GB) of agricultural students; to this end, protection motivation theory (PMT) was used as the core of the theoretical base.

This research method was based on descriptive–correlational and causal–relational analyses. The statistical population included agricultural students of Iranian universities with green university standards (N = 5,582). Out of the total population, 384 students were selected as the study sample. The research instrument was a questionnaire whose validity was confirmed using a panel of experts and the average variance extracted. Also, its reliability was verified by Cronbach’s alpha coefficients (0.61 ≤ α ≤ 0.92), principal component analysis and composite reliability index.

The results of structural equation modeling showed that the obtained model is able to explain 36.3% and 5.56% of GB and GI variance changes, respectively. In addition, the results revealed that GI has the greatest effect on GB (β = 0.362).

It is worth to mention that according to the results, most of the independent variables, besides the direct effects they have on students’ GB, also indirectly affect this variable. This effect was performed through the key variable – GI. In other words, it can be concluded that the GI variable successfully mediates the effects of variables such as response efficacy (RE), self-efficacy (SE) and environmental norms (EN). Therefore, it is suggested that in the behavioral changes interventions in GB of agricultural studies, it should be considered that the presence or absence of GI can affect the actual behavior of individuals. In other words, it is recommended that to accelerate actual behavioral changes, behavioral interventionists should first focus on encouraging people’s GI.

It can be said that the conclusion of this research can provide a basis for the successful encouragement of students to GB. First, GI, as a key element, can mediate the impacts of variables such as RE, SE and EN on students’ GB. Second, PS only directly affects students’ GB. Third, RE has no significant impact on GB, but its effect on GI is significant. Fourth, RC affects students’ GI directly, without mediation. Fifth, SE and EN constructs affect students both directly and indirectly GB of students through GI. Knowing the location of the effect of these variables on each other and the role they have in explaining GI and GB of agricultural students presented some suggestions that can prepare the ground for further development of GB. Hence, managers, students, agricultural educators and other users can use these results to accelerate GB changes.

The conclusion of this research might provide a basis for the successful encouragement of students to GB. In interventions to change GB, it would be essential to pay enough attention to the fact that the presence or absence of GI might affect the actual behavior. It is suggested that behavioral interventionists focus on encouraging people’s GI so as to be able to accelerate the actual behavioral changes.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.

This study aims to develop the understanding of learning processes related to the new ways of interacting in the enforced digital workplace over time.

A multiple, longitudinal case study of knowledge-based workers in three firms located in Sweden has been conducted from March 2020 to March 2023. In total, 89 interviews with 32 employees in three knowledge-based firms have been collected.

The study shows how the intricate interaction between rules and norms for interaction and work must be renegotiated as well as un- and relearned when the physical work environment no longer frames the work context. Furthermore, technology can be viewed as both an enable and a barrier, that is, technology has enhanced collaboration between organizational members yet also created social difficulties, for example, related to communication and interaction. The study emphasizes that individuals learned through trial and error. That is, they tried behaviors such as translating social interactions" to a digital arena, appraised the outcomes and modified the practices if the outcomes were poor.

The present study does have several limitations. First, it is based on interviews with respondents within three organizations in Sweden. To broaden and deepen the understanding of both organizational and learning, future studies can contribute by studying other contexts as well as using a mixed method approach in other countries.

Results from the study can provide a practical understanding of how the rapid change from working at the office to working from home using digital technologies can be understood and managed.

Contributions include combining interaction order and un- and relearning among organizational employees. This insight is important given that the rapid digital transformation of our society has changed how work is performed and how the future workplace will be both structured and organized.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.