Search results

Work readiness is an important aspect of the transition from higher education to professional practice. The purpose of this study was to explore the perceptions of work readiness of individuals transitioning into physiotherapy practice in Australia and identify any association with personal, education and work factors.

Purpose-built surveys were distributed to final-year students and graduates of physiotherapy programmes nationally. Work readiness was measured using the recently validated Work Readiness Scale for Allied Health Professionals 32 (WRS-AH32), which captures the following four domains: Practical Wisdom, Interpersonal Capabilities, Personal Attributes and Organisational Acumen. The surveys also included personal, education and work data. Work readiness was expressed as percentages for total work readiness and within each domain. Independent t-tests were used to examine the influence of personal, education and work factors on work readiness.

176 participant responses were analysed (84 students and 92 graduates). Total work readiness was 80% [standard deviation (SD)8], with Practical Wisdom the highest scoring domain (91%, SD8) and Personal Attributes the lowest scoring domain (65%, SD14). Considering overall work readiness, individuals reporting some psychological symptoms scored lower than asymptomatic individuals [mean difference 7% (95% confidence interval (CI) 4 to 9)] and final-year students scored less than graduates [mean difference 3% (95%CI 0 to 5)].

All stakeholders, including individuals, universities and employers, need to consider further strategies to develop aspects of work readiness, particularly within the domain of Personal Attributes and those with psychological symptoms.

This study demonstrates that physiotherapy students and graduates perceive themselves to be well prepared to transition to the workforce.

India has a marvelous distinction of hosting religious and cultural extravaganzas on an enormous scale, keeping in with its rich lineage and civilizational assortment. The philosophical threads of such festivals are eventually subjective well-being and spiritual awakening. In this context, the authors examine how the visitors' festival motivation culminates in life satisfaction and subjective well-being.

The study follows a theory-driven deductive approach to test the construed relationships. The data were collected from the sites of religious fiesta when the participants were immersed in their cultural fervor. The study uses structural equation modeling to examine the hypothesized model.

The study finds that place attachment and life satisfaction empirically mediate the relationship between festival motivation and subjective well-being. However, the relationship between place attachment and subjective well-being is not empirically strong when life satisfaction mediates their relationship.

The study is based on a convenience sample and is limited to the visitors of local religious festivals. Future research must verify the suitability of the model in other types of festivals of other religions and different locations. Also, this research deliberates on the relationship between only four variables. Future researchers could discuss other variables such as authenticity, emotional solidarity, festival images, festival values, religious faith, etc. to develop a more robust model to explain the relationship between festival motivation and subjective well-being.

In India regardless of social strata, people are religiously conscious and inclined toward attending publicly celebrated religious festivals. The scale of these festivals is significant and given the scenario, the local Government has to join hands with the temple administration, local people and visitors to reap the full benefits of the festival. These temple festivals not only foster coordination and involvement among various stakeholders, but also invoke the devotion of the people to jointly organize the celebrations.

As some of the religious festivals go beyond caste, creed and nationality, the celebrations should evolve as multi-cultural mass events uniting the societal cohesiveness, spirit and national culture. The variables chosen and results found in this study will surely support publicizing the significance of religious festivals in the region and provide an idea to the organizers and supporters to develop new strategies to promote similar events.

The results claim several implications for theory and practice. Theoretically, the study contributes to the literature on religious tourism and event management. Practically, the study discussions indicate the importance of disseminating the significance of religious festivals as a platform for local tourist attractions to generate social, cultural and economic benefits.

Religion plays a crucial role as a sociocultural factor to assess consumer behavior. Stemming from the above, this study aims to analyze the impact of religion and ethnic concern on the purchase intention (PI) of consumers based on the theory of planned behavior.

The research method adopted for this study includes a meta-analysis of the extant literature for the past 20 years focusing on the relationship between religiosity and PI. Data of 24 values from 23 studies were used to assess the impact of religiosity on the PI of consumers.

The findings of this study indicate that religiosity has a strong impact on the PI of consumers. Further, this study identifies that location, sample size and product category play a vital role as moderators toward the relationship between religiosity and PI. This study identifies critical and pertinent implications for brands as they reach out to religious and cultural groups across various geographies, in the context of identifying target markets and adapting marketing strategies.

This study acts in response to the consistent call for research to focus on religion-related variables and fills the gap calling for empirical research into religiosity and its impact on PIs. This study makes notable theoretical, managerial and methodological contributions to the field.

Social media influencers have become constant companions of a large audience of young consumers, but a crucial yet underexplored area of examination relates to the implications of exposure to influencers for an individual’s self-concept. This study aims to examine if and how individuals experience self-discrepancies when exposed to influencers and the impact of such discrepancies on their affect, cognition and behaviors toward the influencers and the brands they endorse.

The authors thematically analyze 17 semistructured interviews, develop a conceptual model and present a set of hypotheses. The hypotheses are tested by analyzing survey data from 503 respondents using structural equation modeling.

Individuals actively engage in comparisons with influencers’ virtual self-presentation and treat them as emblematic of an ideal self. The associated self-discrepancy can lead to both negative and positive affect, but while the latter has a positive impact on e-word of mouth (WOM) and purchase intent, the former has a negative impact. Perceived homophily dampens the impact of exposure to influencer content on discrepancy and strengthens the link between discrepancy and positive affect. Self-acceptance and mindfulness positively moderate the impact of discrepancy on positive affect and negatively on negative affect. Perceived authenticity strengthens the impact of positive affect on e-WOM and dampens the impact of negative affect on purchase intention.

The authors contribute to the literature on self-discrepancies by identifying a consumer context where, in addition to the theoretically predicted negative affect, an individual may experience more positive emotions like feeling motivated or inspired because of the perceived attainability of an influencer as an ideal self. The authors contribute to the influencer marketing literature by examining the influencer–follower relationship and its implications for an individual’s self-concept, including the role played by perceived homophily and authenticity. The authors also contribute to the literature on consumer well-being and identify the role of self-acceptance and mindfulness in shaping consumer experiences.

The authors provide a nuanced analysis of the impact of influencer marketing on consumer behavior with a focus on its impact on an individual’s self-concept. The authors argue for the role of perceived homophily and authenticity in shaping favorable consumer behavior outcomes and offer evidence for more inclusive approaches to marketing.

The authors identify the influencer–follower relationship as a unique social exchange where the source of self-discrepancy is also a homophilic solution provider for achieving one’s ideal self and report both positive and negative effects as outcomes of experiencing a self-discrepancy induced by a target perceived as more attainable. The authors situate understandings of perceived homophily and authenticity along these relationships and identify self-acceptance and mindfulness as mechanisms used by individuals to deal with discrepancies.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

The case is based on heated e-mail conversations connected to the delivery of clean bottled water in the campus of a reputed research institute in southern India. The exchange between Tara Sharma (Programme Manager) and Shreejith Nair (Group Head-Engineering Service and Estate) relate to the quality of services provided. The case highlights the viewpoints of various stakeholders involved in the open conversation. This case focusses on the behaviour of a set of underperforming employees associated with a contractor, the reasons, among others, being lack of training and quality awareness. However, training alone cannot be assumed as the only correct solution for handling underperformance. The stakeholders involved need to ascertain the cause of underperformance by analysing whether it is a “Can't Do” –“Won't Do” problem, and identify the ways of dealing with it.

The purpose of this study is to follow up on the structural and fatigue analysis of car wheel rims with carbon fibre composites in order to ensure the vehicular safety. The wheel is an essential element of the vehicle suspension system that supports the static and dynamic loads encountered during its motion. The rim provides a firm base to hold the tire and supports the wheel, and it is also one of the load-bearing elements in the entire automobile as the car's weight and occupants' weight act upon it. The wheel rim should be strong enough to withstand the load with such a background, ensuring vehicle safety, comfort and performance. The dimensions, shape, structure and material of the rim are crucial factors for studying vehicle handling characteristics that demand automobile designers' concern.

In the present study, solid models of three different wheel rims, namely, R-1, R-2 and R-3, designed for three different cars, are modelled in SOLIDWORKS. Different carbon composite materials of polyetheretherketone (PEEK), namely, PEEK 90 HMF 40, PEEK 450 CA 30, PEEK 450 GL 40 and carbon fibre reinforced polymer-unidirectional (CFRP-UD) are used as rim materials for conducting the structural and fatigue analysis using ANSYS Workbench.

The results thus obtained in the analyses are used to identify the better carbon fibre composite material for the wheel rim such that it gives better structural properties and less fatigue. The R-3 model rim has shown better structural properties and less fatigue with PEEK 90 HMF 40 material.

The carbon composite materials used in this study have shown promissory results that can be used as an alternative for aluminium, steel and other regular materials.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

This study aimed to evaluate the factors that determine an individual's decision to adopt human resources (HR) analytics. This study attempts to extend Unified Theory of Acceptance and Use of Technology - 2 (UTAUT2) to identify the lag rate in adoption.

Responses were obtained from 387 HR employees of the Banking Financial Services and Insurance (BFSI) sector in metropolitan cities of India through nonprobabilistic purposive sampling. The analysis was performed through hierarchical regression, structural equation modeling and moderation of resistance to change.

The results suggest that performance expectancy, hedonic motivation and data availability are endorsed by proponents of the intention to adopt HR analytics. In contrast, effort expectancy, social influence, quantitative self-efficacy and habits did not influence behavioral intention (BI). Additionally, the actual use behavior (UB) of HR analytics was determined by BI and facilitating conditions. Furthermore, the moderating effect of resistance to change is explored.

This study makes a significant contribution to the literature on the adoption of HR analytics. By appropriately concentrating on the adoption intention of HR analytics, organizations can intensify healthy employee relationships, thus encouraging the actual usage of HR analytics.

This study formulates a conceptual framework for the adoption of HR analytics that can be used by top management to formulate strategies for the implementation of HR analytics. Moreover, this study aimed to expand UTAUT2, emphasizing the concept of data availability and quantitative self-efficacy and examining the moderating role of resistance to change in the relationship between BI and UB.

HR analytics is a process for systematic computational analysis of data or statistics. It discovers, interprets and communicates significant patterns in data to enable evidence-based HR research and uses analytical insights to help organizations achieve their strategic objectives. However, its adoption and utilization among HR professionals remain a subject of concern. This study aims to determine the reasons that facilitate or inhibit the acceptance of HR analytics among HR professionals in the banking, financial services and insurance (BFSI) sector.

A sample of 387 HR professionals in BFSI firms across India was collected through non-probabilistic purposive sampling. Structural equation modeling was applied to analyze the association between predetermined variables. In addition, the predictive relevance of “Data Availability” was analyzed using hierarchical regression.

The results revealed that data availability, hedonic motivation and performance expectancy positively influenced behavioral intention (BI). In contrast, effort expectancy, social influence and habit had an insignificant effect on BI. Also, facilitating conditions (FCs), habit, BI achieved a variance of 60% in HR analytics use. The use behavior of HR analytics was significantly influenced by FCs and BIs.

This study focuses on insights into the elements that influence HR analytics adoption, revealing additional light on success drivers and grey areas for failed adoption.

This research adds to the body of knowledge by identifying factors that hinder the adoption of HR analytics in Indian organizations and signifies the relevance of easy accessibility and availability of data for technology adoption.