The purpose of this paper is to examine whether the distribution of police response time to in-progress burglaries differ according to the level of social disorganization across…
Abstract
Purpose
The purpose of this paper is to examine whether the distribution of police response time to in-progress burglaries differ according to the level of social disorganization across different neighborhoods.
Design/methodology/approach
Using 2006 calls for service data collected from the Dallas and Houston Police Departments and from the 2000 US Bureau of Census statistics, the effects of social disorganization on police performance were examined through multilevel analysis of the distribution of police response time patterns across different neighborhoods in Dallas and Houston.
Findings
The analysis of the DPD and HPD in-progress calls produced somewhat consistent findings on the relationship between the level of social disorganization and police response time. Concentrated disadvantage, immigrant concentration, and residential stability are important predictors of the distribution of police response time patterns in Dallas and Houston.
Practical implications
Neighborhood social disorganization is related to the distribution of agency response time patterns. Detailed response time analysis is crucial for agencies to improve police performance and the community-police relationship.
Originality/value
In the policing literature, researchers have tended to neglect rapid response when examining many aspects of policing. The present study expands on existing research by examining the theoretical link between the level of neighborhood social disorganization with the distribution of rapid police response to in-progress burglary in two cities.
Details
Keywords
Abdullah Cihan and William Wells
The purpose of this paper is to examine the correlates of citizens' perceptions about the appropriate amount of police discretion in criminal investigations.
Abstract
Purpose
The purpose of this paper is to examine the correlates of citizens' perceptions about the appropriate amount of police discretion in criminal investigations.
Design/methodology/approach
Using data collected from a random sample of 1,300 households in the USA, this study offers an estimate of citizens' opinions about the amount of discretion granted to police in conducting criminal investigations, and estimates correlations between several predictor variables, including prior victimization and worry about becoming the victim of a crime, and perceptions about police discretion.
Findings
About half of the respondents feel police have the right amount of discretion in conducting criminal investigations. Several variables are correlated with these perceptions, including worry about crime, attitudes about minority civil rights, arrest experiences, and race.
Research limitations/implications
Important police discretionary decisions, like making traffic stops and making arrests, have not been measured. The current study suffers from limitations in the way key concepts are measured. Future research can build on the analysis reported here and make important advancements.
Practical implications
Findings show that important segments of the population believe the police are granted “too much” or “too little” discretion. The results of holding these attitudes remain unknown, but it suggests police can consider ways of addressing differential support for this aspect of policing.
Originality/value
Attitudes about police discretion have generally been omitted from research on opinions about the police. This study offers one attempt to integrate this significant aspect of policing into the existing body of research.
Details
Keywords
Muslim Amin, Abdullah Mohamed Aldakhil, Chengzhong Wu, Sajad Rezaei and Cihan Cobanoglu
The purpose of this paper is to investigate the structural relationships between total quality management (TQM) and employee satisfaction and hotel performance.
Abstract
Purpose
The purpose of this paper is to investigate the structural relationships between total quality management (TQM) and employee satisfaction and hotel performance.
Design/methodology/approach
A judgmental sampling technique was employed in this study. A total of 25 (four- and five-star) hotels were selected in four cities in Malaysia. A total of 625 questionnaires were distributed randomly to both employees and managers.
Findings
The results of this study showed that seven TQM constructs have significant relationships with employee satisfaction and hotel performance. Leadership and customer focus play significant roles in enhancing employee satisfaction and hotel performance.
Practical implications
Employees who are highly satisfied with their jobs will be willing to support their coworkers. They will be loyal to their jobs and enhance hotel performance. Hoteliers must provide a friendly working atmosphere, as well as a blueprint and strategic map, to increase employee satisfaction and improve hotel performance.
Originality/value
This research study provides a substantial contribution to the hospitality management literature by explaining how TQM practices can be used as a predictor of employee satisfaction and consequently improve hotel performance. A better understanding of these relationships will help hoteliers in developing their marketing strategies to maintain the relationship with hotel customers.
Details
Keywords
Faruk Yuksel, Uzeyir Kement, Seden Dogan, Gul Erkol Bayram, Sinan Baran Bayar and Cihan Cobanoglu
This study aims to investigate the effects of smart tourism technology experience (STTE) on tourist satisfaction and happiness in Bordeaux, with a focus on understanding the…
Abstract
Purpose
This study aims to investigate the effects of smart tourism technology experience (STTE) on tourist satisfaction and happiness in Bordeaux, with a focus on understanding the mediating role of self-gratification. By examining these relationships, the study seeks to provide insights into how smart tourism technologies can enhance tourist experiences.
Design/methodology/approach
The study uses partial least squares-structural equation modeling (PLS-SEM) to analyze data collected from 380 tourists who visited Bordeaux. The measurement model assesses reliability and validity, while the structural model evaluates the proposed hypotheses and the mediation effects of self-gratification.
Findings
The results confirm that STTE positively impacts tourist satisfaction, with accessibility, informativeness and personalization significantly enhancing tourist satisfaction, while interactivity does not. Tourist satisfaction, in turn, positively affects tourist happiness. Furthermore, self-gratification partially mediates the relationship between tourist satisfaction and happiness, highlighting its importance in the smart tourism context.
Originality/value
This research extends the understanding of STTE by demonstrating its effects on tourist satisfaction and happiness. It introduces the mediating role of self-gratification, providing a novel perspective on how personalized smart tourism experiences contribute to overall tourist happiness.
Details
Keywords
Aysegul Gunduz Songur, Gozde Turktarhan and Cihan Cobanoglu
The aim of this research, which is based on a literature review and bibliometric analysis, is to reveal the development of green technologies in hotels, based on the articles…
Abstract
Purpose
The aim of this research, which is based on a literature review and bibliometric analysis, is to reveal the development of green technologies in hotels, based on the articles published in tourism and hospitality journals between 1999 and 2020.
Design/methodology/approach
Based on five conditions and five databases, 64 journal papers were retrieved and reviewed. Among the surveyed publications pertinent to the eco-friendly/green technology practices at hotels, the majority focus was on the need for eco-friendly/green technology practices at hotels and the schemes implemented to achieve sustainable development.
Findings
The research findings especially from the last decade report that today's guests generally prefer green hotels based on their increased awareness of environmental degradation and an ever-growing need for conservation and sustainability.
Practical implications
The environmental responsibility which is inherent in the hospitality and tourism industry due to the environmental burden generated by the combined effect of both industries on Mother Earth, brings forth a substantial sense of commitment on the part of hotel companies. In that regard, a set of corporate initiatives in the form of green technology practices are implemented by hotels, toward the development of new product and service offerings, management of processes and corporate policy formation.
Originality/value
This research focuses on green technologies aimed at sustainability in the field of accommodation and tourism, consisting of a systematic literature search on the subject. It is important in the way that it provides a general overview to researchers in terms of the theoretical implications of green technologies while also offering a road map with respect to green technology applications to the practitioners of the field.
Details
Keywords
Amir Masoud Rahmani, Ali Ehsani, Mokhtar Mohammadi, Adil Hussein Mohammed, Sarkhel H. Taher Karim and Mehdi Hosseinzadeh
The concept of e-learning is essential in employee education since it provides different ways to develop employees' knowledge, skills and attitudes using modern technologies…
Abstract
Purpose
The concept of e-learning is essential in employee education since it provides different ways to develop employees' knowledge, skills and attitudes using modern technologies. E-learning has been overgrowing in employee education because learning can be held anytime and anywhere. In order to succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address the factors affecting its success. This paper aims to test the role of internet of Things (IoT)-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework.
Design/methodology/approach
E-learning systems receive ever-increasing attention in academia, business and public administration. With the development of e-learning, employee education has also benefited from its capacities in various fields. To succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address its success. The proposing of Information and Communications Technology (ICT)-based technologies such as the IoT, cloud, etc., in e-learning, can help transform education. Therefore, this paper aims to test the role of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework. The research model and the data collected from the questionnaires have been analyzed via Smart PLS 3.2. This study has utilized the SEM to evaluate the causal model's reliability and validity based on measurement. According to the literature in this study, a framework has been proposed that examines the impact of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on employees' learning programs' success.
Findings
The results have shown that IoT-based systems, cloud-based services, virtual classes and evaluation tools are four significant factors affecting attitude, content management and creativity. The results have also shown that attitude, content management and creativity are three significant factors affecting employees' learning programs' success. The factors above are considered critical in explaining the success of employees' e-learning programs, but, as far as we know, there has been no study in which all these factors were demonstrated together.
Practical implications
From a practical viewpoint, the statistical outcomes support the important role of the following factors: IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity. Henceforth, aspects relating to these factors got the attention of any organization to develop e-learning processes.
Originality/value
This research will contribute to the literature related to employees' e-learning programs' success by integrating all the mentioned variables. As far as we know, it is the first study to test these variables in Iran.
Details
Keywords
Bharat Taneja and Kumkum Bharti
This study aims to examine the research pattern and growth trends of published research on a unified theory of acceptance and use of technology 2 (UTAUT2) from 2012 to 2019. The…
Abstract
Purpose
This study aims to examine the research pattern and growth trends of published research on a unified theory of acceptance and use of technology 2 (UTAUT2) from 2012 to 2019. The study also examines the research scope of UTAUT2 for future researchers.
Design/methodology/approach
This study has adopted a bibliometric approach followed by a structured literature review analysis to synthesize the research on UTAUT2 since 2012. In total, 163 documents were analyzed for type of studies, theories and frameworks, methodologies, author wise collaboration, organizations that contributed to the body of knowledge in the UTAUT2 research and journals that published studies in this domain. VOSviewer and Tableau were used for the data visualization, whereas TCCM, which means theory (T), context (C), characteristics (C) and methodology (M) framework is used to propose the future research directions.
Findings
The findings reveal research on UTAUT2 is growing. The structured literature analysis of the top 15 cited articles further analyzed the parsimony of new models in detail. In addition, the study highlights the inception by and promoters of UTAUT2 in a separate section. The data for this study was collected by searching the title, abstract and keywords of documents in the Scopus database.
Research limitations/implications
This study is based on research papers, published in the UTAUT2 research area, that have been extracted from the Scopus database by keywords only. Future studies can also perform a meta-analysis of various clusters generated by bibliometric analysis.
Practical implications
This study is useful for practitioners to devise strategies for increasing technology acceptance, adoption and utilization in the times to come.
Originality/value
To the best of the authors’ knowledge, this study is one of the very few and early studies, which examined patterns and growth trends of the UTAUT2 studies with the TCCM framework, to suggest scope for future research studies.
Details
Keywords
Ismail Abiodun Sulaimon, Hafiz Alaka, Razak Olu-Ajayi, Mubashir Ahmad, Saheed Ajayi and Abdul Hye
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully…
Abstract
Purpose
Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.
Design/methodology/approach
To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM2.5, PM10, NO2 and O3 in the atmosphere at various times of the day.
Findings
The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.
Research limitations/implications
This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.
Practical implications
This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.
Originality/value
The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.