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1 – 10 of over 5000Alexander Chulok, Maxim Kotsemir, Yadviga Radomirova and Sergey Shashnov
The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within…
Abstract
Purpose
The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.
Design/methodology/approach
The methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.
Findings
As a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.
Originality/value
This research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.
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Kavita Kanyan and Shveta Singh
This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private…
Abstract
Purpose
This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.
Design/methodology/approach
The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.
Findings
The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.
Practical implications
Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.
Originality/value
There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.
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Abdulkareem Salameh Awwad, Abdel Latef Anouze and Elizabeth A. Cudney
This study aims to investigate and test the impact of competitive priorities, in terms of quality, speed, dependability, flexibility, cost and patient engagement, on patient…
Abstract
Purpose
This study aims to investigate and test the impact of competitive priorities, in terms of quality, speed, dependability, flexibility, cost and patient engagement, on patient satisfaction with healthcare services. It considers patients’ rather than managers’ points of view to collect responses about competitive priorities.
Design/methodology/approach
This research employed a cross-sectional survey design to analyze a sample of customers through an empirical study of 488 patients in Qatar’s healthcare service context.
Findings
The confirmatory factor analysis results show that competitive priorities and engagement positively and significantly impact patient satisfaction.
Research limitations/implications
Researchers can use this methodology to explore the role of competitive priorities in different service contexts and sectors. The researchers conducted the study in Qatar; therefore, the results are not generalizable to all healthcare sectors. However, regardless of geographic location, the research approach can be used in healthcare.
Practical implications
Managers can employ the developed scales to diagnose competitive priorities and improve customer service experiences.
Originality/value
The paper is original as it suggests using competitive priorities as a measurement tool for predicting patient satisfaction compared to prior research that mostly measured competitive priorities based on internal perspectives (managers’ perspectives). Further, this paper is original because it depends on the external perspective (customers’ perspective) for the competitive priorities for measuring patient satisfaction.
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Ray Sastri, Fanglin Li, Arbi Setiyawan and Anugerah Karta Monika
The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about…
Abstract
Purpose
The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TME
Design/methodology/approach
This study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.
Findings
The result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.
Originality/value
This study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.
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Ray Sastri, Fanglin Li, Hafiz Muhammad Naveed and Arbi Setiyawan
The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and…
Abstract
Purpose
The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.
Design/methodology/approach
This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.
Findings
The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.
Originality/value
This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.
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Qi Sun, Yaya Gao, Qihui Lu and Yingyi Yan
Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study…
Abstract
Purpose
Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.
Design/methodology/approach
According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.
Findings
Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.
Originality/value
The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.
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Fariba Hosseinpour, Mahyar Seddighi, Mohammad Amerzadeh and Sima Rafiei
This study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a…
Abstract
Purpose
This study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a public tertiary hospital in Qazvin, Iran. This study also aimed to predict influencing factors on patients’ mortality, ICU LOS and hospitalization costs in different admission groups.
Design/methodology/approach
The authors conducted a retrospective cohort study among patients who mainly suffered from internal diseases admitted to an ICU of a public hospital. This study was conducted among 127 patients admitted to ICU from July to September 2019. The authors categorized patients into four groups based on two crucial hemodynamic and respiratory status criteria. The authors used a logistic regression model to predict the likelihood of mortality in ICU admitted patients during hospitalizations for the four prioritization groups. Furthermore, the authors conducted a multivariate analysis using the “enter” method to identify risk factors for LOS.
Findings
Results showed a statistically significant relationship between the priority of being admitted to ICU and hospitalization costs. The authors’ findings revealed that age, LOS and levels of consciousness had a predictability role in determining in-hospital mortality. Besides, age, gender, consciousness level of patients and type of the disease were mentioned as affecting factors of LOS.
Originality/value
This study’s findings emphasize the necessity of categorizing patients according to specific criteria to efficiently use available resources to help health-care authorities reduce the costs and allocate the budget to different health sectors.
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Marco D’Orazio, Gabriele Bernardini and Elisa Di Giuseppe
This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information…
Abstract
Purpose
This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).
Design/methodology/approach
This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.
Findings
The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.
Research limitations/implications
This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.
Practical implications
The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.
Social implications
The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.
Originality/value
This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.
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Kelley A. Packalen, Kaitlyn Sobchuk, Kelly Qin-Wang, Jenelle Cheetham, Jaclyn Hildebrand, Agnieszka Fecica and Rosemary Lysaght
The goal of this study was to understand which employee-focused workplace practices and priorities – more formally known as human resource (HR) practices and priorities …
Abstract
Purpose
The goal of this study was to understand which employee-focused workplace practices and priorities – more formally known as human resource (HR) practices and priorities – employees with mental health and/or addiction challenges (MHAC) valued and how they perceived the day-to-day implementation of those practices and priorities in the workplace integration social enterprises (WISEs) that employed them.
Design/methodology/approach
Twenty-two WISE workers who self-identified as having serious MHAC participated in semi-structured interviews. Interviews were transcribed and coded to identify ways that employees did or did not feel supported in their WISEs.
Findings
Participants identified three HR practices and two HR priorities as important to establishing an inclusive workplace that accommodated their MHAC. The extent to which individual participants felt included and accommodated, however, was shaped by interactions with their supervisors and coworkers.
Originality/value
By evaluating the salience of WISEs’ employee-focused workplace practices and priorities through the lens of the employees themselves, our study articulates the critical role that interactions with coworkers and supervisors have in determining whether HR practices and priorities have the intended effect on worker experience.
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Érico Daniel Ricardi Guerreiro, Reginaldo Fidelis and Rafael Henrique Palma Lima
A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.
Abstract
Purpose
A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.
Design/methodology/approach
This study proposes the Primary Transformation Model (PTM) and an equation to measure cause-and-effect relationships between productivity and competitive priorities.
Findings
The interdependence between productivity and competitive priorities was studied using the PTM and the proposed model indicates that strategies that improve external performance also impact internal productivity. It was also observed that the compatibility between competitive priorities depends on the initial manufacturing conditions and the implementation method adopted.
Research limitations/implications
The proposed model is theoretical and, as such, is an abstraction of reality and does not consider all possible aspects. It consists of a novel approach that still requires further empirical testing. The PTM provides insights about the trade-offs between productivity and strategic objectives, as well, contributes to the ongoing research on manufacturing strategy and can be further developed in future studies.
Practical implications
The main practical implication is to allow companies to relate their strategic decisions to their productivity performance.
Social implications
This research also contributes to societal issues by enabling firms to better align strategic objectives and operations, which ultimately allows offering products more suited to the needs of customers, thus making better use of the required resources and favoring economic growth.
Originality/value
The model proposed allows objective assessment of actions aiming at operational efficiency and effectiveness, in addition to providing insights into cause-and-effect relationships between productivity and competitive priorities. The model can also be used in empirical investigations on manufacturing strategy.
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