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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.

The present research aims to investigate how lean project management (LPM) and agile project management (APM) affect project performance outcomes in the construction sector in Jordan. This study focuses on six key project performance outcomes, namely cost, time, quality, client satisfaction, innovation and responsiveness.

The present study employed a quantitative approach to achieve the research objectives. Accordingly, a multi-item survey questionnaire was prepared to gather data from 392 project managers from construction companies in Jordan. The study’s model showed acceptable levels regarding reliability, validity, fit indices and discriminant validity. In order to test the hypotheses of this study, path analysis was employed using Amos 24.0 software.

LPM demonstrated a remarkably high positive impact on cost performance. It also positively affected quality performance and client satisfaction. However, LPM insignificantly affected time, innovation and responsiveness performance measures. On the other hand, APM showed a notably high positive impact on innovation and responsiveness. The findings also revealed that APM positively impacted quality performance and client satisfaction. In addition, APM negatively impacted cost performance and insignificantly impacted time performance.

This study is one of the first comprehensive studies to empirically examine the impact of both LPM and APM on various project performance outcomes in the construction industry in the context of a developing country. It reveals some similarities and differences between LPM and APM with regard to their impacts on project management outcomes. The findings are expected to guide managers in selecting the appropriate project management approach based on the desired performance outcomes. Accordingly, it offers important implications for project managers in construction companies.

Supply chain (SC) and knowledge management (KM) have been studied; still, there is a need to understand how KM can be used for SC resilience and improving the firm’s performance. The purpose of the paper is to study and analyze SC resilience strategies based on KM processes to enhance SC performance considering six SC strategies: SC reengineering, collaboration, SC innovation, SC integration, SC agility and SC risk management.

By adopting the dynamic capability theory, the empirical research is conducted on a sample of 312 Indian micro, small to medium enterprises. To evaluate 312 samples, the structural equation modeling approach is adopted.

The study found a is a positive relationship between SC reengineering, SC collaboration, SC integration, SC agility, SC risk management and KM. Nevertheless, the relationship between SC innovation and KM is not significant. This study also found the mediating effect of KM on SC performance, and the results shows that SC reengineering, SC collaboration, SC agility and SC risk management are having complementary mediation, while SC innovation and SC integration did not show any mediation.

This is the only research that integrates resilience strategies and KM for improving SC performance. Using KM, SC reengineering will improve SC performance by enhancing readiness and recovery strategies to avoid SC disruption. KM will improve SC collaboration. It will enhance the SC process’ overall visibility, transparency and so on. Agility leads to increased speed, visibility and flexibility, which aids in dealing with uncertainty in the environment. SCRM entails investments and additional resources (such as equipment and labor) to navigate uncertainty and risks in the SC and improve SC performance.

This study aims to examine the challenges facing the construction industry practitioners toward adopting digital construction technologies in the Jordanian construction industry.

Quantitative methods were used by reviewing the related literature to include 16 challenges that face the Jordanian construction industry in adopting digital construction. A questionnaire was used to achieve the desired study objectives for 373 respondents from various institutions and companies. The questionnaire was analyzed with SPSS using statistical tests such as mean score, Kruskal–Wallis H test and factor analysis.

After collecting the quantitative data, the study showed that the most challenges facing construction industry practitioners toward adopting digital construction techniques are lack of qualified workers, high requirement for computing equipment’s, high initial cost of bringing these technologies to the market and construction firms low investment in research and development. These challenges faced by respondents were divided into three main factors, namely, construction’s nature, financial constraints and poor management support.

This study provides information and statistics on the challenges that face individuals or companies toward adopting digital construction techniques in Jordan. It proposes recommendations and proper practical implantation strategies to overcome the challenges.

Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.

Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.

In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.

Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.

The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

In a recent quasi-experimental study, the effects of a large German public startup support measure entitled “EXIST – Business Startup Grant” (EGS) on a variety of outcomes were determined, but without examining which factors are responsible for these program effects. The present study investigates the contribution of several factors to the success of the program in promoting product development and business planning.

By means of a two-wave panel design and fixed-effects panel regressions, evidence is generated that provides unique insights into the effect mechanisms of a publicly funded startup grant. The data for the study come from the program monitoring of the startup support measure.

Several factors were identified that significantly drive the effects of the program on the product development and business planning stages, namely the program-induced improvement of the skills of the startup team, intensification of cooperation with pilot customers/users, increase in the degree of networking and advice/support from third parties and the effort put into business plan preparation.

Startup support programs are a crucial aspect of technology and innovation policies, which are often evaluated in order to find out whether they generate effects. Assessing whether a program is effective or not, however, does not usually allow specific recommendations on how to improve the measure to be developed. Further information on the mechanisms of intervention is needed for this purpose. The present study takes up on this idea and provides this information for a specific type of public startup support measure.

Drawing on absorptive capacity theory, this study explores the impacts of supply visibility and demand visibility on product innovation (i.e. exploratory and exploitative innovation), and it examines how supplier integration, customer integration and internal integration mediate these impacts.

The authors employ empirical survey data from 200 Chinese manufacturers and use structural equation modeling to test the proposed relationships.

The results show that supply visibility is positively related to supplier integration and internal integration and that demand visibility is positively related to customer integration. Furthermore, only customer integration and internal integration positively relate to exploratory and exploitative innovation.

First, this study emphasizes that supply visibility and demand visibility are important sources of a firm's innovation performance and that supply chain integration increases focal firms' capability of exploiting information and facilitates product innovation. Second, the study shows that supply visibility and demand visibility have distinct effects on three dimensions of supply chain integration and exploratory and exploitative innovation. The study also provides significant managerial guidelines for effectively leveraging supply chain visibility and integration in the promotion of product innovation.

The purpose of this paper is to investigate whether organizational support for innovation and informational extrinsic rewards moderate the relationship between intrinsic motivation and innovative work behavior.

Multiple and hierarchical regression analyses based on data from 150 knowledge workers tested the hypotheses for a South African sample.

The results confirmed a positive relationship between intrinsic motivation and innovative work behavior, and found positive relationships between both organizational support for innovation and informational extrinsic rewards and innovative work behavior. While organizational support positively moderated the relationship between intrinsic motivation and innovative work behavior, acting in synergy with intrinsic motivation, informational extrinsic rewards had a negative moderating effect.

When organizations want to encourage knowledge workers to generate, promote and realize innovative ideas, they should create an environment that encourages autonomy, competence and relatedness, with support for creativity and differences of ideas.

The study provides new indications of the interactions of synergistic extrinsic rewards and intrinsic motivation to affect innovative work behavior.

For the design of this case study, the authors used primary sources of information from the shops visited by them in preparation of the case and website of Fred Perry and secondary sources of information from both academic and journalistic publications.

Fred Perry is a premium clothing brand, well-known for its polo shirts. It was created by Mr Fred Perry, a British tennis player. The brand’s stated values are integrity, personality and individuality. Throughout its history, the brand has been adopted by different British subcultures but recently it has faced a challenge because of the brand appropriation by the Proud Boys, a US far-right white supremacy group and other extremist groups as Antifa and hooligans. The nature and actions of the group mean that Fred Perry runs the risk of losing control over its brand equity. This brand hijack means that Fred Perry risks alienating some of its customers by openly opposing the group but also by embracing this subculture’s appropriation. Practically, the brand opposed the appropriation in a press release and by putting an end to the sale of the black and yellow polo shirts in the USA and Canada. Fred Perry has also made a lot of efforts to reposition the brand away from extremist groups while maintaining its strong historical and cultural roots. Through this case study, students will have the opportunity to discuss this topic and explore solutions for brands that face this type of dilemma.

This case is designed to be used in a marketing management, brand strategy or consumer behavior/culture course, especially in the subfield of market segmentation in the telecommunications sector. Specifically, this case is designed for college seniors or master students with basic strategic marketing training. This case will help students understand the difference between the brand identity that the brand owners intend and the brand image that consumers actually perceive. It provides the basis of discussions on the topics of brand management, consumer culture, consumers-brands relationships, brand architecture, brand equity, brand appropriation and repositioning strategy.

This study aims to characterize the behavior of blended concrete, including metakaolin (MK) and quarry dust (QD), as supplementary cementing materials. The study focuses on evaluating the effects of these materials on the fresh and hardened properties of concrete.

MK, a pozzolanic material, and QD, a fine aggregate by-product, are potentially sustainable alternatives for enhancing concrete performance and reducing environmental impact. The addition of different percentages of MK enhances the pozzolanic reaction, resulting in improved strength development. Furthermore, the optimum dosage of MK, mixed with QD, and mechanical properties like compressive, flexural and split tensile strength of concrete were evaluated to investigate the synergetic effect of MK and quarry dust for M20-grade concrete.

The results reveal the influence of metakaolin and QD on the overall performance of blended concrete. Cost analysis showed that the optimum mix can reduce the 7%–8% overall cost of the materials for M20-grade concrete. Energy analysis showed that the optimum mix can reduce 7%–8% energy consumption.

The effective utilization is determined with the help of the analytical hierarchy process method to find an optimal solution among the selected criteria. According to the AHP analysis, the optimum content of MK and quarry dust is 12% and 16%, respectively, performing best among all other trial mixes.