Search results

The time is right for renewed and updated attention to the relationship between public relations (PR) and human resources (HR) departments in the context of corporate social responsibility (CSR) and sustainability. For too long, conflict between the two practice areas has obscured opportunities for collaboration which benefits organizations and stakeholders. This chapter offers theoretical underpinnings for examining an interdepartmental, cross-unit working relationship between HR and PR – and advances a vision for why it is needed now.

Massive shifts in the recruitment landscape, the continually changing nature of work and workers, and extraordinary technological progress have combined to enable unparalleled advances in how current and prospective employees receive and process information about organizations. Once the domain of internal organizational public relations and human resources (HR) teams, most employment branding has moved beyond organizations’ control. This chapter provides a conceptual framework pertaining to third party employment branding, defined as communications, claims, or status-based classifications generated by parties outside of direct company control that shape, enhance, and differentiate organizations’ images as favorable or unfavorable employers. Specifically, the authors first theorize about the underlying mechanisms by which third party employment branding might signal prospective and current employees. Second, the authors develop a framework whereby we comprehensively review third party employment branding sources, thus identifying the different ways that third party employment branding might manifest. Third, using prototypical examples, the authors link the various signaling mechanisms to the various third party employment branding sources identified. Finally, the authors propose an ambitious future research agenda that considers not only the positive aspects of third party employment branding but also potential “dark sides.” Thus, the authors view this chapter as contributing to the broader employment branding literature, which should enhance scholarly endeavors to study it and practitioner efforts to leverage it.

The research surveyed the competency of information literacy of senior high school students in Fiji. This is to evaluate the strong predictors of information literacy.

The study adopted a survey research design whereby a five-point Likert scale self-reporting questionnaire was administered to Year 12 and Year 13 secondary school students. The data were analysed using the Statistical Package for the Social Sciences (SPSS) software-descriptive statistics of calculating the mean and standard deviation, a correlation and linear regression analysis to deduce the strong predictors of information literacy.

The study showed that 81% of the students surveyed were average to above average information literate. The strong predictors of information literacy were the ability of an individual to collaborate and share safely online, the ability to share files securely and the ability to access the credibility of any resource assessed on the digital platform.

The current study evaluates information literacy of a cohort – stating how information literate the participants are, comprehending the strong predicators of information literacy so that there is an appropriate and effective implementation of interventions for the desired improvements.

The results can be used to improve information literacy of students at all levels of education in the Fiji Islands.

If the youths are information literate they will be able to effectively contribute towards the development of their economy. Since the work environment today is technology oriented and involves a lot of information, being information literate means knowing how to use the information and differentiate between good and bad information. Hence, contributing effectively towards whatever task is performed.

This research if the first ever research done on evaluating the information literacy of individuals in Fiji.

This paper aims to explore the key factors which influence whistleblowing intentions of teachers working with higher education institutions (HEIs) in India. Both internal and external whistleblowing intentions of the HEI teachers are studied by examining their relative intentions to report a potential wrongdoing to the authorities within the management of the institution and to the external statutory bodies. The reporting intentions of the HEI teachers are measured through the use of three vignettes related to academic frauds. Whistleblowing intentions are proposed to be determined by the individual, organizational and situational factors.

Survey method of descriptive research design has been used to obtain the primary data regarding the individual, organizational and situational variables deemed to influence HEI teachers’ internal and external whistleblowing intentions. A self-administered structured questionnaire is used as survey instrument for primary data collection. The respondents’ internal and external whistleblowing intentions are measured through the use of three vignettes related to academic frauds. Non-parametric tests such as Mann–Whitney U test, Kruskal–Wallis test and Spearman correlations have been used to test the research hypotheses.

The study has found that the HEI teachers are more likely to blow the whistle internally if there is a proper communication channel in their organization for reporting wrongdoings. However, they do not hesitate to blow the whistle externally in the absence of internal reporting channel, especially in those cases of wrongdoings where they perceive the cost of reporting to be high. The high status of the wrongdoer and high costs of reporting discourage the teachers to blow the whistle internally. However, if the wrongdoer holds a very powerful position in the organization, then the teachers prefer to report his wrongdoing to external agencies as they are afraid of the likely negative repercussions of reporting against him internally. In case of serious wrongdoings, the teachers intend to blow the whistle within the organization rather than going to external agencies probably because they do not want to spoil the image of their organization in the external world.

The first limitation is that because of the unavailability of pre-tested vignettes in the context of academic frauds, the study has used three vignettes which have been developed on the basis of few case studies. Second, the results showed the existence of social desirability bias across all the three vignettes. Also, the study has been conducted among teaching professionals; therefore, the findings cannot be generalized to the professionals of other sectors.

The findings of the study may bring awareness to the board of management of HEIs, regarding the importance of whistleblowing in their educational institutions. They should encourage the teachers working with their institutions to report the wrongdoings internally as external reporting may cause damage to their institute’s reputation. Proper reporting mechanisms should be designed and shared with the employees as a part of institutional culture.

The Whistle Blowers Protection Act passed by the Parliament of India in 2011 should be amended to include the private sector employees, especially the teachers working in higher education sector. This will encourage the HEI teachers to report the academic frauds fearlessly which will put a serious check on the growing number of frauds and wrongdoings in the education sector.

Previous research studies have discussed the factors influencing whistleblowing intentions in the context of various non-academic organizations. However, existing research has not adequately provided a better understanding of the influencing factors of whistleblowing intentions in higher education sector. The present paper addresses this gap by empirically examining the key factors which influence HEI teachers’ intentions of blowing the whistle and reporting the wrongdoings occurring in their institutions, in Indian context. An attempt has been made to identify the influencing factors of both internal and external whistleblowing intentions by using three different vignettes related with academic frauds.

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

The purpose of this paper is to analyse health service supply chain systems. A great deal of literature is available on supply chain management in finished goods inventory situations; however, little research exists on managing service capacity when finished goods inventories are absent.

System dynamics models for a typical service‐oriented supply chain such as healthcare processes are developed, wherein three service stages are presented sequentially.

Just like supply chains with finished goods inventory, healthcare service supply chains also show dynamic behaviour. Comparing options, service reduction, and capacity adjustment delays showed that reducing capacity adjustment and service delays gives better results.

The study is confined to health service‐oriented supply chains. Further work includes extending the study to service‐oriented supply chains with parallel processing, i.e. having more than one stage to perform a similar operation and also to study the behaviour in service‐oriented supply chains that have re‐entrant orders and applications. Specific case studies can also be developed to reveal factors relevant to particular service‐oriented supply chains.

The paper explains the bullwhip effect in healthcare service‐oriented supply chains. Reducing stages and capacity adjustment are strategic options for service‐oriented supply chains.

The paper throws light on policy options for managing healthcare service‐oriented supply chain dynamics.

Hydrological and climatological modeling is increasingly being used with the intent of supporting community-based climate change adaptation (CCA) and disaster risk reduction (DRR) initiatives in the Hindu Kush-Himalaya (HKH), as well as filling critical data gaps in a region that contributes significantly to the water resources and ecosystem diversity of Asia. As the case studies presented in the previous chapters illustrate, the utility of modeling in informing and supporting CCA and DRR initiatives depends on a number of criteria, including:•appropriate model selection;•ability to interpret models to local contexts; and•community engagement that incorporates and addresses underlying vulnerabilities within the community.

There are significant challenges to meeting all three of these criteria. However, when these criteria are met, we find:•There is a clear role for modeling to support CCA. The climate is changing now and will continue to do so for several centuries, even if carbon emissions were to stabilize tomorrow. Models, and other scenario development tools, provide our best insight into what the future climate might be and resulting impacts on dynamic social, environmental, political, and economic systems.•There is a clear role for local CCA. The impacts of climate change will be felt mostly at local levels, necessitating community adaptation responses. At the same time, most of the HKH communities and countries engaged in CCA initiatives have pressing, immediate development and livelihood needs. Making current development and livelihood initiatives incorporate climate adaptation considerations is the best way to ensure that the choices made today can set us on paths of increasing resilience, rather than almost inevitable disaster, for the future.•To achieve the best of both modeling and CCA requires thoughtful and patient application of modeling, tailored to local needs, conditions, and politics, with communities engaged around all stages of generating, interpreting, and applying the results. This requires a rare combination of technical skill, cultural sensitivity, political awareness, and above all, the time to continually engage with and build relationships within the community in order to foster resilient change.

The purpose of this methodology is to categorise the challenges into cause and effect group. The modern scenario of customization, personalization and multi-restrictive working because of pandemics has affected the operations of manufacturing small and medium enterprises (SMEs). In the new normal, the digitalization of manufacturing SMEs can be the path breaker. Modern digitalization includes a mix of technologies such as the industrial internet of things (IIoT), the internet of things, cyber-physical system and big data analytics. This digitalization can help in achieving new design changes, efficient production scheduling, smart manufacturing and unrestricted on-time delivery of quality products. This research paper aims to recognize and analyze the challenges faced while implementing IIoT technologies in manufacturing SMEs and tries to find the possibility of mitigating challenges by blockchain technology.

There were ten challenges of IIoT implementation identified from the literature review and experts’ opinions. To collect information from Indian manufacturing SMEs, a survey tool was formed in the form of a questionnaire. On the fundament of responses received from industrial experts, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique has been used for categorizing these challenges into cause and effect groups. Further, the authors tried to mitigate observed challenges with the help of blockchain technology.

With the implementation of IIoT technologies, the manufacturing processes become conciliatory, effective and traceable in real time. Observation of the current study states that the top effect group challenges such as the security of data and reliability of technologies can be mitigated by enabling blockchain technologies. The authors conclude that blockchain-enabled IIoT technologies will be highly beneficial for the Indian SMEs strategically and practically in the current scenario.

Methodology of DEMATEL focuses on responses received from experts. The broader approach of survey from manufacturing organizations is compromised due to small sample size in this methodology. Experts approached for survey were from manufacturing SMEs of Delhi National Capital Region only. Broader survey-based techniques may be applied covering different sectors of SMEs in future work.

Technologies such as blockchain can facilitate advanced security in the application of IIoT and other such practices. While dealing with significant issues and challenges of new technologies, blockchain gives an edge of balance in the current scenario. Its properties of fixity, temper evident and circumvent fraud make this technology ideal for the digitalization of the manufacturing systems in SMEs.

Digitalization of manufacturing facilities is the need of the hour. Pandemic challenges have highlighted the urgency of it. This research will motivate and guide the manufacturing SMEs in planning strategies and long-term policies in implementing modern technologies and coping up with the pandemic challenges.

This paper aims to develop a parsimonious and innovative model that captures the dynamics of new product diffusion in the recent high-technology markets and thus assist both academicians and practitioners who are eager to understand the diffusion phenomena. Accordingly, this study develops a novel diffusion model to forecast the demand by centering on the dynamic state of the product’s adoption rate. The proposed study also integrates the consumer’s psychological point of view on price change and goodwill of the innovation in the diffusion process.

In this study, a two-dimensional distribution function has been derived using Cobb–Douglas’s production function to combine the effect of price change and continuation time (goodwill) of the technology in the market. Focused on the realistic scenario of sales growth, the model also assimilates the time-to-time variation in the adoption rate (hazard rate) of the innovation owing to companies changing marketing and pricing strategies. The time-instance upon which the adoption rate alters is termed as change-point.

For validation purpose, the developed model is fitted on the actual sales and price data set of dynamic random access memory (DRAM) semiconductors, liquid crystal display (LCD) monitors and room air-conditioners using non-linear least squares estimation procedure. The results indicate that the proposed model has better forecasting efficiency than the conventional diffusion models.

The developed model is intrinsically restricted to a single generation diffusion process. However, technological innovations appear in generations. Therefore, this study also yields additional plausible directions for future analysis by extending the diffusion process in a multi-generational environment.

This study aims to assist marketing managers in determining the long-term performance of the technology innovation and examine the influence of fluctuating price on product demand. Besides, it also incorporates the dynamic tendency of adoption rate in modeling the diffusion process of technological innovations. This will support the managers in understanding the practical implications of different marketing and promotional strategies on the adoption rate.

This is the first attempt to study the value-based diffusion model that includes key interactions between goodwill of the innovation, price dynamics and change-point for anticipating the sales behavior of technological products.

In India, the COVID-19 outbreak has been declared an epidemic in all its states and union territories. To combat COVID-19, lockdown was imposed on March 25, 2020 which has adversely affected the education system in the country. It has changed the traditional education system to the educational technologies (EdTechs) model, where teaching and assessments are conducted online. This paper aims to identify the barriers faced by teachers during online teaching and assessment in different home environment settings in India.

Interpretative phenomenological analysis (IPA) of qualitative research methodology has been used in this research. The study was conducted among the teachers working in the government and private universities of Uttarakhand, India. Semi-structured in-depth interviews were conducted among 19 teachers to collect data regarding the barriers faced by them during online teaching and assessment. ATLAS.ti, version 8 was used to analyze the interview data.

The findings revealed four categories of barriers that are faced by teachers during online teaching and assessments. Under home environment settings, a lack of basic facilities, external distraction and family interruption during teaching and conducting assessments were major issues reported. Institutional support barriers such as the budget for purchasing advanced technologies, a lack of training, a lack of technical support and a lack of clarity and direction were also reported. Teachers also faced technical difficulties. The difficulties were grouped under a lack of technical support, it included a lack of technical infrastructure, limited awareness of online teaching platforms and security concerns. Teachers’ personal problems including a lack of technical knowledge, negative attitude, course integration with technology and a lack of motivation are identified as the fourth category to damper their engagement in online teaching and assessments.

The findings of the study can be helpful to the regulatory authorities and employers of higher education institutions who are planning to adopt online teaching as a regular activity in the future. The insights gained from the findings can help them to revisit their existing policy frameworks by designing new strategies and technical structures to assist their teachers in successfully embracing the EdTech to deal with any crisis in the future.

Many authors have conducted research to address the problems faced by students related to online teaching and learning during COVID-19 in India. To the best of the authors’ knowledge, this is the first study that addresses the challenges faced by teachers during the online teaching and assessment in the home environment settings by using qualitative analysis (IPA) techniques. The current study replenishes the gap by contributing to the literature of online teaching and assessment under the home environment settings during the pandemic situation.