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The research aims to establish importance scheme of geospatial factors for land valuation activities that may serve as an eye-opener and aid the concerned government agencies in drafting land valuation policies and guidelines to achieve a sound land governance and administration. It specifically identifies and weighs geospatial valuation factors to establish their importance.

The research involves discussions and survey questionnaires given to land experts (i.e. appraisers, environmental planners, land economist, geodetic engineers and assessors) who indicated their opinions on influence of geospatial factors on land value. The analytic hierarchy process (AHP) is then used to weigh the factors in terms of its importance.

The result was then compared with the multiple regression analysis (MRA) taking into consideration the standardized regression coefficient of the 15 factors. The AHP method found out the major road accessibility and slope direction as the most and least influential factors, respectively, while surprisingly MRA found major road accessibility not significant at p < 0.05 level of significance.

The research generally reflects the sub-urban type of study area; hence, inclusion of other road types such as express ways and subways and performing sensitivity analysis of AHP are suggested in future studies.

The findings of the study will provide information of concerned government agencies in improving valuation activities, as well as to update values regularly based on the geospatial factors.

To the best of the authors’ knowledge, this study is the first effort to rank geospatial factors with analytic hierarchy analytic process that further considered both their negative and positive influences on land value. The approach surmounts the flaw and shortcomings of empirical methods of identifying importance of factors.

The purpose of this paper is to compare some major National Quality Award/Business Excellence Models (NQA/BEM) in terms of the criteria employed and their relative weights. It shows that these models vary both in terms of criteria and their weights. Whereas some of them are changing weights frequently, others are almost static. It employs the analytic hierarchy process (AHP) to allocate scores to 12 criteria identified in the model by Agrawal et al. (1998) to propose a modified quality award model similar to that. The six quality award models used in the USA, Canada, Europe, Australia, Japan and India are compared with the proposed model using AHP and their relative rankings are obtained.

First, a literature review is done to identify various quality award models globally, with their features being compared. Furthermore, paired comparison technique is used to rationalize the relative weights of proposed 12 criteria, and then AHP is again used to rank this proposed model with six major award models.

This paper shows that the six NQA models vary substantially on parameter weights. They do not include some relevant criteria to evaluate the organizational performance holistically. It also reveals how some models have been revising criteria weights very frequently, whereas others are static. In some models, the results get much higher weightage than enablers, and hence the performance may not be sustainable. The modified Agrawal et al. (1998) model is taken as a base model, with weights rationalized in it using the AHP. The rankings obtained using AHP reveal that proposed model scores over the other six prominent quality award models. The result also reveals that for organizational excellence, the quality of people plays a major role in the successful implementation of quality processes. Hence, it is very important to focus on improving the quality of people before expecting improvement in the quality of products and services.

The paired comparison results are based on the researchers’ own perception and do not consider interdependence among the criteria, which is a limitation of AHP. Analytic network process can be further explored to overcome the limitation. The proposed model has not been tested in a variety of real-world situations, which can constitute a scope for further work in the direction.

The proposed model framework and weightages evolved using AHP can provide a universally acceptable quality award model framework. The companies can adopt it with or without modifications to address their contextual adaptation. It can possibly become a standard model framework globally. This model does not capture the measurement of the softer aspects that impact the people quality. As people play an important role in the success of the implementation of any practice, hence measurement of people quality is another important aspect that can be further studied and researched.

This comparative study & analysis of National Quality Award/Business Excellence Models using AHP is presented for the first time. The authors have not come across any such studies in their literature review. This paper is an original conceptualization of the application of the AHP on the various Quality Award model parameters, and it has been submitted exclusively to JAMR for publishing.

The present study primarily aims to investigate the role of the high-performance work system (HPWS) in shaping learning orientation (LO). Moreover, the study delves into the examination of affective commitment's (AC) role as a mediator. Additionally, the research extends to exploring the potential moderating impact of workplace settings, specifically offline, online and hybrid workplaces, on the mediated relationship.

The model has been tested on 360 respondents from the IT sector in India. The data have been validated using confirmatory factor analysis and structural equation modeling.

The results indicate that AC mediates the relationship between HPWS and LO. Furthermore, this mediation was found to be positive and significant in hybrid and offline workplace settings, whereas it was found to be insignificant in the online workplace setting.

The self-report responses from the respondents could potentially introduce a limitation, as respondents might lead to inflated outcomes in the research findings. While Harmans’ single-factor test did not indicate any common method bias, it is important to acknowledge that this bias might not have been completely eliminated.

The present study has expanded upon the existing research on HPWS by examining its influence on LO, while also considering AC as a mediator. Furthermore, it is noteworthy that no prior studies have undertaken a comparative analysis of this relationship across various workplace settings.

This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

Digital technologies have been found to have long-lasting impacts on poverty. This study has been conducted to shed light on the contributions made by digital technologies toward poverty alleviation and also provide future research directions.

The authors thoroughly studied the sample of 258 publications from the Scopus database, covering the period from 1982 to June 2023. Using VOS viewer and Bibliometrix R, various graphs and networks are developed to understand publication trends, research collaborations and intellectual structures.

A significant amount of the existing literature on the impact of digital technologies on poverty alleviation demonstrates the need for more studies in this area. Lack of information and communication technology (ICT) infrastructure and access exhibits less opportunity for work, social networking and entrepreneurship, directly affecting people’s livelihoods in developing countries.

This study will help country planners, regulatory bodies and academicians get a deeper insight into the impact of digital technologies on poverty alleviation and also develop the future research agenda.

This study employs a considerable period of time, from the year 1982 to June 2023. To the best of authors’ knowledge, the current study is a pioneer in using bibliometric analysis to identify the impact of digital technologies on the alleviation of poverty. This attempt will surely be helpful to academicians, researchers and those working on identifying the impact of digital technology on poverty alleviation.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

The purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.

The authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.

The presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.

Presented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.

The case focuses on truck high idle time, truck detention, and enroute challenges faced by Nagpur Golden Transport Company, a trucking company with a fleet size of 150 trucks. Being in a highly competitive market, every single order not fulfilled by the transporter is an opportunity lost. How should NGTC use GPS data, MIS, and other freight transport technological capabilities in reducing truck idling times, and increasing trip revenues?

This paper aims to report an insightful portable microfluidic system for rapid and selective sensing of Hg²⁺ in the picomolar (pM) concentration using microcantilever-based piezoresistive sensor. The detection time for various laboratory-based techniques is generally 12–24 h. The majority of modules used in the proposed platform are battery oriented; therefore, they are portable and handy to carry-out on-field investigations.

In this study, the authors have incorporated the benefit of three technologies, i.e. thin-film, nanoparticles (NPs) and micro-electro-mechanical systems, to selectively capture the Hg²⁺ at the pM concentration. The morphology and topography of the proposed sensor are characterized using field emission scanning electron microscopy and verification of the experimental results using energy dispersive X-ray.

The proposed portable microfluidic system is able to perform the detection in 5 min with a limit of detection (LOD) of 0.163 ng (0.81 pM/mL) for Hg²⁺, which perfectly describes its excellent performance over other reported techniques.

A microcantilever-based technology is perfect for on-site detection, and a LOD of 0.163 ng (0.81 pM/mL) is outstanding compared to other techniques, but the fabrication of microcantilever sensor is complex.

Many researchers used NPs for heavy metal ions sensing, but the excess usage and industrialization of NPs are rapidly expanding harmful consequences on the human life and nature. Also, the LOD of the NPs-based method is limited to nanomolar concentration. The suggested microfluidic system used the benefit of thin-film and microcantilever devices to provide advancement over the NPs-based approach and it has a selective sensing in pM concentration.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.