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This study aims to explore employee-customer identification and its consequences in the banking, financial service and insurance (BFSI) sector. We also look at the mediating role of psychological ownership and work orientation (calling) between employee-customer identification and the adaptability of service offerings.

In this study using a sample of 215 frontline employees from the BFSI sector in five Indian cities, the data was analysed using partial least squares structural equation modelling (PLS-SEM) in Smart PLS- 3.2.7 software.

When employees consider customers as individuals similar to them, they tend to be more accommodating of customers' diverse needs resulting in adapting the service. The study empirically establishes that psychological ownership and work orientation (calling) mediate the relationship between employee-customer identification and service offering adaptation.

This kind of identification can remedy the perennial problem of mis-selling in the BFSI context. The new insights gathered from these customer interfaces can be transferred upwards within the organisation to formulate actionable strategies. Hence, when employees feel their work is satisfactory, it leads to improvement in both profit margins as well as asset turnover for high-contact service firms.

The results demonstrate that employees who identify with their customers are more accommodative of customers' diverse needs resulting in adapting the service resulting in improved performance.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Employee unsafe behaviours and failure to adhere to safety standards resulting from poor safety perception among employees contribute to the high rate of accidents in the construction industry. This study seeks to examine the role of respectable engagement in improving construction workers' safety perceptions in the Ghanaian Construction Industry.

The study adopted a quantitative research method where survey questionnaires were administered to respondents. Sixty-six construction workers who were actively involved in ongoing construction projects in Kumasi and Greater Accra regions of Ghana were selected through stratified purposive sampling. The analytical tools utilised in the data analysis include a one-sample t-test, descriptive statistics and mean score ranking.

The study identified five key drivers and strategies of respectable engagement from pertinent literature. The findings confirmed that all these drivers and strategies play a key role in improving workers' safety perception. The study further revealed that improving employees' safety perception will enhance and sustain their awareness of the organisation's commitment to health and safety. Employees will therefore portray positive safety behaviour by adhering to the safety standards of their organisation.

The findings of this study will contribute to construction site safety improvement by informing contractors, site supervisors and other stakeholders of their role and the need to improve their worker's safety perception.

This research is unique in that; it identifies the role of respectable engagement in improving construction workers' safety perception. This research creates awareness among management and site supervisors on the need to be present for their workers, affirm them, attend to their needs, understand and appreciate them, and communicate and listen to them.