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Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

Upon the premises of social exchange theory (SET), this study aimed at hypothesizing and examining a serial mediation model that investigated the underlying mechanism through which a high-performance work system (HPWS) affects individuals’ future time perspective (FTP).

The hypothesized relationships were examined using responses collected from 275 employees from 15 local private banks and 40 established branches through a proportionate stratified sampling technique. The statistical package for social sciences (SPSS) PROCESS macro 3.0 and analysis of moment’s structure (AMOS) 24.0 were employed for data analysis purposes.

The study revealed that HPWS is indirectly related to the individuals’ FTP through workplace social courage (WSC) and employee well-being (EWB) sequentially. Prescriptions for theoretical and managerial implications were discussed, and future research viewpoints with limitations were acknowledged.

This study illuminated the underlying mechanism and theoretical logic linking HPWS and individuals’ FTP by proposing the serial mediating effect of WSC and EWB.

The purpose of this study is to investigate the impact of integrating “Licorice powder” into curd balls on their storage stability under refrigeration conditions. Through this examination, this study aims to evaluate the potential effects of licorice powder on extending the shelf life, maintaining quality attributes and preserving the overall stability of curd balls when stored at refrigeration temperatures.

Licorice powder, in varying quantities (1%, 2% and 3%), was incorporated into curd balls alongside a control group lacking licorice (0%). These batches were subsequently stored for 25 days under refrigeration at a temperature of 4 ± 1ºC, using aerobic packaging conditions. During this storage period, the samples were regularly monitored and analyzed for various parameters to assess changes in their properties and qualities.

The findings indicated that in the treatment groups, pH and titratable acidity were notably lower than those in the control group (p = 0.05). Curd balls enriched with licorice powder exhibited significantly higher levels of 2, 2-diphenyl-1-picrylhydrazyl, 2-2-azinobis-3ethylbenthiazoline-6-sulphonic acid and total phenolic contents compared to the control (p = 0.05). Furthermore, curd balls containing licorice powder displayed notably lower levels of peroxide, thiobarbituric acid reactive substances and free fatty acids in comparison to the control (p = 0.05). Among all samples, T3 (3%) demonstrated significantly less microbial growth (p = 0.05) than the other groups. Conversely, the sensory panel rated T2 significantly higher than T3 (p = 0.05).

The investigation highlights that curd balls enriched with 2.0% licorice powder demonstrated significant efficacy in preventing the deterioration of physicochemical attributes, enhancing antioxidant capacity, restraining lipid oxidation, curbing microbial growth and ultimately exhibiting the most favorable organoleptic properties among the tested variations. This finding underscores the potential of incorporating 2.0% licorice powder as an effective agent for bolstering the storage stability and overall quality of curd balls during refrigerated storage.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

This study aims to explore and understand the challenges and opportunities presented by the rising demand for organic products in the context of toddy consumption and marketing.

This research examines consumer behaviour and decision-making patterns using decision tree analysis. A survey questionnaire based on established theories was distributed to individuals above the legal drinking age of 23 in Kerala, India, using purposive and random sampling.

The study found that people's fondness for toddy shop food plays a crucial role in their food choices. When the fondness is low, subjective norms can override personal preferences. But when the fondness is high, individual perceptions take precedence.

Using machine learning techniques, we created a compass to guide marketing strategies and cultural preservation efforts in toddy shops by considering the complex factors that influence consumer decisions.