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The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

The objective of this research is to examine the association of artificial intelligence (AI) awareness on workers' retention and the boundary conditions in the context of project organizations.

We collected time-lagged data from project organizations in China.

The results showed that AI awareness predicted workers' turnover intention. Moreover, this association was moderated via supervisor support.

This research provides several practical implications aimed at timely communication, training and automation guide for helping firms to foster healthy workplace climate, support and workers’ retention.

The rise of AI and its potential impact on manpower is a popular topic. Yet, the evidence of project workers’ awareness of such potential effects on their retention is scant. Therefore, this study broadens our understanding of the association of AI awareness on turnover intention and boundary conditions in the context of project organizations.

While the COVID-19 pandemic has given many firms the importance of business continuity, restaurants have yet to realize the essential enablers. In this regard, the study explored the potential enablers that can help firms attain business continuity in the post-COVID-19 era for casual dining restaurants.

This study used an extensive literature review and further supported 28 restaurant managers with an average experience of 8.5 years to identify the potential enablers for ensuring business continuity for casual dining restaurants. Subsequently, this study used the interpretive structural modelling coupled with Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis to understand the context-specific inter-relationships as a hierarchical structural model.

Findings suggest that financial capability forms the key driver for other enablers in the hierarchy of business continuity for casual dining restaurants. Furthermore, manpower (local and outstation) are the second-level drivers of restaurant process control, digital presence, online reviews and feedback management, scalability, food quality, food delivery management, employee safety and hygiene. The third-level factors further help restaurants to achieve the dynamic capability required to provide customer value and ensure business continuity.

The study is the foremost to explore a business continuity framework in the new normal for casual dining restaurants in the country. Furthermore, several contextual inter-relationships exhibiting the hierarchy are also established for the business continuity of restaurants in the new normal.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.

Within the umbrella of technology and vocational education (TVET), technology or technical education in higher institutions of learning is obligated to produce the required manpower needed in the industry. Thus, it is pertinent to explore the interaction between the industry and higher education students. Drawing on the tenets of theory of planned behavior (TPB), this study offers valuable insights into the nomological networks of work-integrated learning (WIL), perceived behavioral control (PBC), subjective norm (SBN), personal attitude (PAT) and job search intention (JSI).

The study applied a structurally hypothesized model that was drawn from the TPB to collect data for the constructs. Using a cross-sectional survey after the WIL experiences of the students, we collected data from technology education undergraduates (N = 214) in their final academic year from universities in Nigeria.

With structural equation modeling, the study found that WIL is directly associated with JSI, PBC, SBN and PAT. In line with the tenets of the TPB, simple mediation models were supported about the influence of WIL on JSI via PBC and PAT discretely but not via SBN. Further, the results support two paths of serial mediation models, indicating sequential indirect links between WIL and JSI via SBN and PBC, as well as via SBN and PAT.

Our findings have implications for higher education practitioners, industry experts and employers of labor.

Although extant literature has relatively shown that WIL impacts employability skills, this study has remarkably shown the WIL-JSI nexuses within the variables of TPB.

This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.

The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.

The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.

The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.

In Bangladesh (BD), delays in infrastructure are common. Many previous studies have explored the causes of infrastructure delays. However, this study investigated the causes of delays by taking responses from the stakeholders who are responsible for planning, design, funding, approval and implementation. There are few studies that have related infrastructure project delays to heterogeneity in stakeholders’ perceptions.

A structural equation (SE) model is developed with 350 normally distributed data points to understand the heterogeneity in stakeholders’ perceptions regarding delays in infrastructure projects in BD. Additionally, the relative importance index (RII) approach is used to assess the responses, validating the SE model.

The study finds that among the three latent variables, “Project itself related delay” has more influence on delays in infrastructure projects. Among the observed variables under the “project itself related delay” latent variable, “DPP approval process” has the most significance. From the heterogeneity analysis, the study found differences in responses among the stakeholders from “the Engineering Department,” “the Planning Office” and “the Construction Firm/Industry.” An important class of stakeholders believes that their stage is not being delayed and that other stages require attention.

The data sample is 350. More data can improve the accuracy of the findings. Most of the respondents are civil engineers (74%) and represent the owner of the project. Sample data from more stakeholders’ will enhance the accuracy of the result.

This study addresses the requirements of Bangladeshi project stakeholders and how their interactions cause delays in projects. Furthermore, the opinions of other stakeholders are taken into consideration when determining the specific factors of individual stakeholders that are causing delays. Practically, the distance between stakeholders should be reduced. A project manager can play a role in this regard. Initiatives should be taken on how to complete the project quickly by eliminating the requirements discussed among the stakeholders and bureaucratic complications. Instead of placing blame on one another, stakeholders should take the initiative to figure out how to work together to finish the project on schedule. The Planning Commission’s approval of the Development Project Proposal (DPP) and Revised Development Project Proposal (RDPP) should be obtained as soon as possible by owner stakeholders. In order to avoid frequently changing the DPP, owners should also exercise greater caution when choosing contractors. Contractor stakeholders should use efficient and proper manpower and equipment so that unexpected delays are not created during the execution of work. Since the role of the contractor stakeholder is the most important among the three types of stakeholders, the contractor should raise awareness and urge the owners to get the RDPP approved quickly.

The findings from the study can help mitigate delays in infrastructure projects in BD, taking into account the perceptions of various stakeholders.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

The purpose of this research is to examine the relationship between employer and employees in a public power sector undertaking through the validation of CODE (compensation, organizational justice, dispute resolution and employee empowerment) and PLE (workforce productivity, employee loyalty and employee engagement) model.

A hypothesized research model was developed and validated by using structural equation modeling (AMOS 20). In total, 303 responses were accumulated by administering a structured questionnaire among the employees of a state-owned power sector.

The results revealed that a harmonious climate of employee relations is prevailing in the public power utility. Additionally, the findings suggest that the CODE and PLE model of employee relations are found to be positive and significant by investigating the impact of compensation, organizational justice, dispute resolution and employee empowerment as the predictors; and workforce productivity, employee loyalty and employee engagement as the critical outcomes of employee relations.

The study recommends some plausible insights for practitioners, decision-makers and policy formulators to develop strategies and policies for nurturing congenial employee relations and also to cultivate a facilitative work environment for generating contented and competent manpower.

Validation of CODE and PLE model of employee relations in the new perspective of power sector undertaking is an epoch-making and novel contribution that offers significant empirical evidence to the extant literature. Moreover, the exploration of employer–employee relations in this context is a unique and innovative effort toward existing research.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.