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Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

Adoption of integrated MOS based pseudo-resistor (PR) structures instead of using off-chip passive poly resistors for analog circuits in complementary metal oxide semiconductor technology (CMOS) is an area-efficient way for realizing larger time constants. However, issue of common-mode voltage shifting and excess dependency on the process and temperature variations introduce nonlinearity in such structures. So there is dire need to not only closely look for the origin of the problem with the help of a thorough mathematical analysis but also suggest the most suitable PR structure for the purpose catering broadly to biomedical analog circuit applications.

In this work, incremental resistance (IR) expressions and IR range for balanced PR (BPR) structures operating in the subthreshold region have been closely analyzed for broader range of process-voltage-temperature variations. All the post-layout simulations have been obtained using BSIM3V3 device models in 0.18 µm standard CMOS process.

The obtained results show that the pertinent problem of common-mode voltage shifting in such PR structures is completely resolved in scaled gate linearization and bulk-driven quasi-floating gate (BDQFG) BPR structures. Among all BPR structures, BDQFG BPR remarkably shows constant IR value of 1 TΩ over −1 V to 1 V voltage swing for wider process and temperature variations.

Various balanced PR design techniques reported in this work will help the research community in implementing larger time constants for analog-mixed signal circuits.

The PR design techniques presented in the present piece of work is expected to be used in developing tunable and accurate biomedical prosthetics.

The BPR structures thoroughly analyzed and reported in this work may be useful in the design of analog circuits specifically for applications such as neural signal recording, cardiac electrical impedance tomography and other low-frequency biomedical applications.

The purpose of this paper is to identify and examine the important factors that could affect consumers' behavioural intention and use behaviour towards mobile payment services during COVID-19.

The proposed model extends meta-Unified Theory of Acceptance and Use of Technology (meta-UTAUT) model with perceived severity and self-efficacy factors affecting consumers' behavioural intention and use behaviour towards mobile payment services. A convenient sampling technique has been utilized to gather data from a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation.

The findings revealed that performance expectancy, effort expectancy and perceived severity have a significant positive impact on consumers' attitude; facilitating conditions has a significant positive impact on effort expectancy; self-efficacy has a significant positive impact on effort expectancy; attitude has a significant positive impact on behavioural intention; and behavioural intention has a significant positive impact on use behaviour. Social influence did not confirm any significant relationship.

The current research study has utilized a non-probability convenient sampling technique to gather data through a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation. The respondents were adopters of mobile payment services. The scope of the study is the COVID-19 context or related chronic diseases context where major preventive mechanisms such as social distancing and avoidance of physical contacts are vital.

This study has extended the meta-UTAUT model with the COVID-19 context-specific constructs and relationships. The undertaken work has strengthened the explanability of the model. The inclusion of context relevant variables such as perceived severity and self-efficacy and their association with the existing meta-UTAUT framework have enriched the context of the study. The current study offers a holistic understanding of significant factors influencing Indian consumers’ adoption of mobile payment services in the COVID-19 context.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

The purpose of this paper is to summarize the scientific information of various qualities of bael fruit juice used in traditional system of medicine for variety of purposes. Utilization of bael fruit juice in day-to-day life has great nutritional, therapeutic, and commercial importance. Bael fruit contains nutrients like vitamins (riboflavin), minerals, trace elements, energy and phytochemicals, including flavonoids, polyphenols and antioxidants, that have been shown to have varied health benefits. In past few decades, bael has been extensively studied for its medicinal properties by advanced scientific techniques, and a variety of bioactive compounds like marmelosin, tannins, alkaloids, coumarins, steroids, rutacine, y-sitosterol, psoralin, xanthotoxin, scopolotein, aegelemine, aegeline, marmeline, fragrine, dictamine, cinnamide and different derivatives of cinnamide have been isolated from its fruit juice.

The medicinal value of bael fruit is very high when the harvests just begin to ripen. As a result, it has a high demand as alternative medicine for curing the diseases like diabetes, high cholesterol, peptic ulcer, inflammation, diarrhea and dysentery, constipation, respiratory infection. Furthermore, the bael fruit juice has anticancer, cardio protective, antibacterial, antifungal, radio protective, antipyretic, analgesic, antioxidant, antiviral, anthelmintic and anti-inflammatory, hepatoprotective, wound healing properties. The ripe fruit juice is aromatic, has cooling and laxative effects, and arrests secretion or bleeding.

The unripe or half-ripe fruit juice is good for digestion, useful in preventing or curing scurvy, and it strengthens the stomach action. It helps in the healing of ulcerated intestinal surfaces and has appreciable activity against intestinal pathogenic organisms. The present review summarizes the scientific information of various qualities of bael fruit juice used in traditional system of medicine for a variety of purposes.

It is quite evident from this review that bael is an important medicinal herb and extensively used in Ayurveda, Siddha and other medicinal systems. Bael fruit juice is an excellent source of water and natural sugar and is important principally for containing vitamins, minerals, phytochemicals, antioxidants, pigments, energy, organic acids, dietary fiber and other food components, which are the key factors in the medicinal value of this plant. Moreover, mechanisms of action of a few bioactive compounds have been identified so far.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

In this study, the authors have tried to estimate and examine the relationship between energy intensity (EI), capacity utilisation (CU) and total factor productivity (TFP) of Indian Iron and steel industry for the time period 1980–1981 to 2016–2017. The estimation of TFP and its growth is based on three inputs capital (K), labour (L) and energy (E) using transcendental logarithmic cost function. Besides, capital and labour factor inputs, the estimation of CU also take into account the input, energy. It also considers the variable, price of energy and uses a variable cost (VC) function. This study also takes into account the concept of adjusting total factor productivity growth (TFPG) by CU. From our result, this study observes that CU increased over time. Again, Iron and Steel industry has become energy intensive (increasing EI) over the time period taken up for the study. On the other hand, TFPG has declined in the liberalised era. The preliminary idea that we get from our result is that TFPG is negatively related to the extent of pollution generated by energy intensive nature of the Indian Iron and Steel industry, which is in line with reality. To come out of this situation, we may prescribe the use of green technology and green energy sources for this industry.

Oxidative rancidity is a major problem in chicken meat because of its higher content of polyunsaturated fatty acids. Besides this, microbial contamination is also one of the major problems of chicken meat which leads to deterioration in quality during storage. Therefore, the purpose of this paper is to investigate anti-microbial and anti-oxidant effects of various blends of essential oils (EOs) on the quality of emulsion-based (ready-to-eat) chicken sausages.

Sausages were incorporated with optimum level of four different blends of EOs containing six different EOs (Clove oil, Holybasil oil, Thyme oil, Cassia oil, Ajowan oil and Beetel oil), namely, Blend-1 (0.25 per cent), Blend-2 (0.25 per cent), Blend-3 (0.25 per cent) and Blend-4 (0.125 per cent); vacuum packaged and stored at −18±1°C for 60 days. Duplicate samples were taken for each parameter, and three trials were conducted for each experiment, total being six observations (n=6) for consistency of the results.

Significant decrease (p<0.05) in pH of control products was observed at each interval of storage period; however, in case of treatment products, significant decrease (p<0.05) was noticed from day 30 onwards. Blend-2 was observed with significantly lower (p<0.05) thio-barbituric acid reacting substances followed by Blend-1. Significantly lower (p<0.05) total phenolics content was observed in Blend-4 products as compared to other treatments. Regarding DPPH activity, control products showed significant decrease (p<0.05); however, in case of treatment products, DPPH activity showed significant (p<0.05) decrease after day 15 of storage. Microbial count increased with progressive storage period; however, the counts were well below the permissible limit of frozen meat products. All the blend incorporated products received very good sensory scores in consistent manner.

The work under this study would be very useful for the meat processing industries dealing with the perishable meat products. Use of natural anti-oxidants will also restore faith towards the consumers who are becoming more health conscious day by day.

The research work is original.

Based on the stimulus-organism-response (SOR) framework, this study aims to investigate the effect of social media marketing (SMM) activities on consumers’ purchase intention (PI), as well as to test the mediation effect of consumer-based brand equity (CBBE) and consumer inspiration (INS) between the relationship of SMM and PI. Further, this study has also proposed and validated SMM as a reflective–formative higher-order construct (R-F-HOC) with its five first-order dimensions: customization, entertainment, interaction, trendiness and word of mouth (WoM).

Using a non-probability purposive sampling method, a structured questionnaire survey using Google forms was used to collect data from a sample of 236 UAE consumers. Subsequently, the data was analyzed with a hybrid method that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis.

The findings suggest that SMM has a direct effect on CBBE, INS and PI. Both proposed mediation effects are statistically significant, and there is a partial complementary mediation effect of CBBE and INS between SMM and PI. This study validated the operationalization of SMM as R-F-HOC. Further, the results of the ANN analysis validate the results of the PLS-SEM, suggesting that SMM is the strongest predictor of PI followed by CBBE and INS.

In terms of theoretical significance, this study has advanced our understanding of the process by which the influence of SMM is transferred to PI via CBBE and INS. This study has also made a significant contribution by validating SMM as a R-F-HOC. In terms of practical implications, this study suggests that SMM should be best assessed as a R-F-HOC construct with five dimensions: customization, entertainment, interaction, trendiness and WoM. This study has also demonstrated the importance of CBBE and INS in transmitting the effect of SMM on PI to marketers.

This study contributes to the digital advertising literature by filling a knowledge gap about the mediation effect of CBBE and INS between SMM and PI via the SOR framework. SMM’s multidimensionality as a R-F-HOC has also been established.