Search results

The purpose of this paper is to explore the various forms of organizational and team-level actions that were perceived to be helpful or not helpful by employees as they navigate the hybrid work arrangements and how these had an impact on their work behaviors and experiences. This research utilized Bronfenbrenner's ecological systems theory as framework.

The exploratory study used a qualitative approach in gathering data via online survey from a total of 45 Filipino employees working in a hybrid work arrangement for at least three months. The analysis utilized both inductive and deductive methodologies in examining the data. Inductive thematic analysis was used in coding the data based on the participants' responses, while the deductive approach ensured that the themes are aligned with the research questions and reflect the different systems within Bronfenbrenner's EST (1986).

Results surfaced helpful organizational (e.g. provision of work tools, financial assistance, supportive policies and engagement and wellness initiatives) and team level actions (i.e. use of technology-based communication tools, open virtual door policy, effective performance management system, employee care practices and team engagement activities). Actions that were perceived as not helpful include inadequate technological infrastructure, poor communication, insufficient training, punitive policies/practices and leadership issues at the organizational level as well as unresponsive colleagues and ineffective implementation of policies/processes at the level of teams. Employees reported being able to build on savings, becoming more productive and having greater work–life balance amid hybrid work. However, they continue to be challenged by blurred boundaries and inability to disconnect from work similar to when work was done remotely and now with sustaining momentum given the shifts on where they do their work.

The findings of this study may guide programs and initiatives of human resource management practitioners and organizational leaders as they support employees in navigating through hybrid work.

The research expands extant knowledge on practices and experiences in hybrid work (Gifford, 2022). It also contributes to studies on human resource management that are nuanced based on where work is performed (Ng and Stanton, 2023) or with emerging work arrangements.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.

This paper aims at understanding the causes of low adoption of hybrid rice technology. The paper also assesses the impact of adoption of hybrids and modern varieties on crop yield, vis-à-vis the old or traditional varieties.

Using unit-level data from a large-scale survey of farm households (19,877 paddy cultivators), the authors applied multi-nomial regression method to understand the factors for adoption of hybrid rice and instrumental variable method of regression to estimate its impact.

The findings demonstrate that in India, hybrid rice is often grown on relatively poor soils, resulting in greater irrigation costs and for other inputs, such as fertilizers. Further, farmers' poor access to information on the traits of hybrid rice and the associated agronomic practices, as well as poor access to financial resources, hampers efforts to scale up its adoption. More importantly, the findings reveal that the relative yield advantage of hybrids over open-pollinated modern varieties is not large enough to incentivize the rapid adoption of hybrid rice technology.

Given the higher cost of hybrids than the inbred varieties, enhancing paddy cultivators' access to financial resources can accelerate the adoption of hybrid rice in India.

The study is based on unit level data from a large-scale, nationally representative survey of farm households, comprising a sample of 19,877 paddy cultivators, spread across states in India.

This paper aims to propose, the multi-objective method for optimal planning and operation of distributed generators (DGs) on distribution system (DS) using hybrid technique is proposed.

The proposed hybrid technique denotes hybrid wrapper of black widow optimization algorithm (BWOA) and bear smell search algorithm (BSSA). BWOA accelerates the convergence speed with combination of the search strategy of BSSA; hence, it is named as improved black widow-bear smell search algorithm (IBWBSA) technique.

The multiple-objective operation denotes reducing generation cost, power loss, voltage deviation with optimally planning and operating the DS. For setting up the DG units on DS, IBWBSA technique is equipped to simultaneously reconfigure and find the optimal areas.

In this planning model, the constraints are power balance, obvious power flow limit, bus voltage, distribution substation’s capacity and cost. Then, proposed multiple-objective hybrid method to plan electrical distribution scheme is executed in the MATLAB/Simulink work site.

The aim of this study is to explain the educational practices that emerged with the concept of “new normal” in an overview. In line with this goal, the experiences of academics who are experts in their fields have been utilized.

This paper draws on both academic and trade literature. Interviews with representatives from schools providing tourism education in North Cyprus were conducted online and in-person. The qualitative research method in the form of the interview method was employed in the research. The research participants consist of 15 academics working in the field of tourism education. Additionally, concerning the selection of these participants, sector experience and applied lecture status were taken into consideration. As the second dimension of the research, an extensive literature review was conducted and the subject was expanded by explaining it with examples from around the world.

It has been revealed in the research that educational institutions and tourist businesses have to constantly renew themselves technologically. The findings obtained from academics include the innovations they use in the “new normal” tourism education in applications such as Hybrid/BL – Blended Learning, VR – Virtual Reality, Artificial intelligence, LMS & In-class response systems, API-based software systems in the courses they teach. The common opinion of academics who want to provide a better education is that educational institutions should always be infrastructurally prepared for such emergencies.

The global COVID-19 pandemic created economic destruction in many countries and brought life to a halt. In the tourism sector, one of the sectors most affected by the COVID-19 problem, various restrictions were imposed on touristic activities. While this situation caused a decrease in demand in the tourism sector, it paved the way for great technological changes and resulted in the adoption of new educational practices in institutions providing tourism education. This paper traces these new development in the face of the pandemic crisis.

This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.

This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).

GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.

Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

The Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO₂ reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.

Three scenarios for reducing energy consumption and CO₂ emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.

The first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.

Pilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.

The proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).

All three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.