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The faculties are crucial to education. They should have enough training facilities and be encouraged to actively contribute to high-quality education and successful learning. Faculty engagement and development activities should be explored and included in learning organizations and employee engagement in India. This paper aims to describe higher education as a learning organization. The research will also assess how faculty development programs affect faculty engagement behaviors in these institutions and if professional development mediates this effect, which has not been previously examined.

This study was conducted with quantitative data collected from 267 faculties through reliable and validated adapted questionnaires. Semistructured interviews were conducted with heads and professor-level faculties to gain insights into faculty development and engagement. Partial least squares structural equation modeling technique (PLS-SEM 3.3.6 version) was used to test the conceptually drafted model.

Faculty professional development programs shown to improve higher education faculty engagement and professional progress. The studies also showed that higher education institutions must prioritize faculty development to become learning organizations. Professional development reduced the direct effects of faculty development program (FDP) on faculty engagement. This suggests that professional growth mediates the research.

This research emphasizes and professional development to boost teacher involvement in B-Schools. Management must design faculty development programs to construct professional development and learning organizations, according to the results. Developing and writing rules that encourage faculty engagement in such internal and external programs would also enhance their academic and administrative abilities and assist higher education institutions become learning organizations.

The study is one of the few to examine the impact of faculty development programs and professional development on faculty engagement in higher education institutions, particularly B-Schools, and its competitive mediating role.

The potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.

A mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.

The findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.

Specific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.

This study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs.

This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result.

This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested.

The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI.

This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies.

This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.

This research investigated the impacts of COVID-19 on construction enterprises and good practices adopted by the enterprises in reducing COVID-19 risks. The Sendai Framework (TSF) is widely accepted as a strategic roadmap to reduce disaster risks throughout the life cycle of a disaster. As such, with the aim of enhancing the resilience of Australian construction enterprises, the identified good practices were mapped with TSF priorities to consolidate COVID-19 risk reduction practices that can be adopted by Australian construction enterprises.

Case study research approach was used, and three case studies were conducted with small, medium and large construction enterprises. Small, medium and large enterprises were selected based on the Australian Bureau of Statistics classification of the business size. Data were collected through semi-structured interviews conducted with three executive members from the three enterprises. Data were analysed using content analysis.

The study found that construction enterprises faced demand and supply side impacts. Infrastructure projects, funded by public sector clients and larger enterprises were least affected. Investments and demand for residential and other building projects were reduced by private sector clients, affecting small and medium enterprises. Findings also show that the construction enterprises adopted good practices in identifying, managing, investing on resilience and recovery that align with TSF priorities. All three enterprises agreed on some common good practices on risk identification, risk management and effective recovery. Different views were shared on investments related to disaster resilience.

This study contributes to mitigate the COVID-19 impacts on construction enterprises and subsequent economic and social impacts.

This research found how Australian construction enterprises survived during COVID-19. The study adopted TSF to construction and COVID-19 context while consolidating COVID-19 risk reduction practices.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

Using real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.

A case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.

Operator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.

This is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

The main objectives of this study are to (1) explore underlying components and attributes of travel applications that influence traveler usage intention and (2) investigate which contents should be prioritized to increase the efficiency of travel apps.

This study uses importance–performance map analysis (IPMA) with an innovative approach (i.e. generalized structured component analysis [GSCA]) to identify components and attributes that have relatively high and low significance for the target construct (i.e. travel app's usage intention).

An integrated model influenced by three components (i.e. entertainment, travel review and medical and emergency information is proposed to have an impact on travelers' intention to use travel apps). The IPMA results indicate that restaurant and shopping receive the utmost emphasis from Thai domestic tourists who use travel apps, and sports and attraction are regarded as less critical attributes.

The app content scales identified in this study can be used as a basic framework for app developers, service providers and tourism authorities to design a travel app. The IPMA results also help concerned practitioners to make decisions on which contents/attributes should be incorporated and improved.

This study implements IPMA using a GSCA estimator to present the standard results reporting on the path coefficients of each of the attributes of travel app services. The results not only contribute to the improvement of app features and services, but also suggest an innovative approach for generating pertinent IPMA results with GSCA, thus contributing to the theoretical and methodological advancement in hospitality and tourism areas.

In the era of Industrial Revolution (IR) 4.0, the integration of digital technologies, automation and data-driven insights has generated a broad wave of transformation across all industries, including the insurance sector. The study focuses on determining how the adoption of these technologies (InsurTech) is changing the life insurance industry, ultimately enhancing the level of customer satisfaction.

The data analysis has been performed with 304 useable responses from the policyholders of life insurance in the north-west region of India. The methodology adopted for this study is partial least squares (PLS) structural equation modeling (SEM). To investigate the predictive relevance of customer satisfaction, the PLS predict technique has been used. Also, importance performance map analysis (IPMA) has been applied to assess the important and performing dimensions of customer satisfaction.

The outcomes show that the adoption of InsurTech has a positive impact on customer satisfaction. Customer service management and policy management are among the strongest predictors of customer satisfaction, and the predictive relevance is reported to be moderate. IPMA results have suggested that improvements in online distribution of insurance services and customer service management lead to higher customer satisfaction.

The conceptual model can be tested with the moderating effect of different demographic factors (age, gender etc.), and future research can be done to analyze the mediating role of customer satisfaction between InsurTech adoption and customer loyalty.

The study offers valuable contributions to the marketing literature, shedding light on the influence of InsurTech adoption on customer satisfaction within the Indian life insurance sector. The research offers a practical approach that could help marketing professionals and policymakers comprehend the utilization of online insurance services, and this understanding can help industry experts to develop customer-oriented products and services.

This research is the first of its kind to test the association between InsurTech adoption and customer satisfaction in the life insurance sector in the Indian context. Research also provides novel insights for policymakers to enhance the satisfaction of customers towards using online insurance services in the near future in developing countries like India.

The Oxford Strategy Insights Project was designed to assess the current approaches, aims, and focus of strategists across industries and geographies.

The project received input from 167 executives and managers across twenty-six industries, spanning over thirty countries, regarding their firm’s strategy process.

The biggest gap highlighted by the Oxford Strategy Insights Project is effective strategy execution. An overwhelming majority of respondents indicated that their organization’s strategy execution efforts are ineffective and slow, with accelerated implementation being the exception rather than the norm. In line with these findings, few firms appear to be applying the best practice of establishing an implementation management infrastructure including a “Program Manager” tasked to manage and coordinate the firm’s strategy implementation effort. The one positive element related to strategy execution is that most firms seem to have an effective strategic measurement and reporting process.

Organizations that can execute their strategies increase the likelihood of realizing the full potential of their plans. However, the study’s main finding is that effective strategy execution is severely lacking. Strategists need to become more foresighted, with a much greater focus on implementation.

Although the value of effective strategy execution has been well documented for over five-decades in both academic and management practice literature, new research has found that most strategists are still short-sighted, viewing strategy as primarily planning with a limited focus on implementation.

This study aims to create a more humane and responsible workplace, individuals’ gratitude and meaningfulness seem of utmost importance. This study is an effort to understand the role of gratitude intent of potential managers.

This study examines the psychological characteristic of business students in India. The researchers surveyed 333 Indian students as future managers. The collected data has been analysed with the Smart PLS 3 version to assess the formative-reflective scale by comparing model fit, measurement model and structural modelling.

The results establish that gratitude significantly affects the life satisfaction of future managers. Findings also show that materialism is negatively related to life satisfaction and meaningfulness. The importance–performance map analysis finding suggests that meaningfulness in life is a potential indicator of life satisfaction for the population studied.

Due to the limited research available on the psychological underpinnings in the Indian context, there is a massive value in examining how materialism and gratitude concurrently and distinctively predict meaning in life and the life satisfaction of future managers. This paper gives a formative explanation of the model consisted gratitude, materialism and meaningfulness in life on the life satisfaction of future managers. This study establishes the importance of meaningfulness of life in attaining life satisfaction for young managers.