Pub Date : 2023-01-01DOI: 10.1177/18479790231188242
M. Alshurideh, B. Al Kurdi, Haitham M. Alzoubi, Iman A. Akour, Samer Hamadneh, A. Alhamad, Shanmugan Joghee
Maintaining durable and long-lasting relationships with customers is a key factor that is widely considered by marketing practitioners and company management. Therefore, this study aims to explore and examine the factors (personal interest, trust, safety perceptions, E-transaction acceptance, and privacy concerns) influencing electronic relationship ER from the customers’ perspectives. The study selected the sample from university students (456 respondents) and was conducted in United Arab Emirates UAE, to analyze their perspectives about these factors. The study findings found significantly positive effect of all these factors on ER. And the most influential one was the personal interest followed by trust. Our research concludes by mentioning customers’ communication experiences and perceptions with their companies in order to assess their ability to meet expectations and maintain ongoing relationships. The research implications offer the marketing practitioners with insight to diversify their interaction ways with their key customers.
{"title":"Factors affecting customer-supplier electronic relationship (ER): A customers’ perspective","authors":"M. Alshurideh, B. Al Kurdi, Haitham M. Alzoubi, Iman A. Akour, Samer Hamadneh, A. Alhamad, Shanmugan Joghee","doi":"10.1177/18479790231188242","DOIUrl":"https://doi.org/10.1177/18479790231188242","url":null,"abstract":"Maintaining durable and long-lasting relationships with customers is a key factor that is widely considered by marketing practitioners and company management. Therefore, this study aims to explore and examine the factors (personal interest, trust, safety perceptions, E-transaction acceptance, and privacy concerns) influencing electronic relationship ER from the customers’ perspectives. The study selected the sample from university students (456 respondents) and was conducted in United Arab Emirates UAE, to analyze their perspectives about these factors. The study findings found significantly positive effect of all these factors on ER. And the most influential one was the personal interest followed by trust. Our research concludes by mentioning customers’ communication experiences and perceptions with their companies in order to assess their ability to meet expectations and maintain ongoing relationships. The research implications offer the marketing practitioners with insight to diversify their interaction ways with their key customers.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90160761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231174318
Wei-gang Fan, Xiang Wu, Xin Yang Shi, Chong Zhang, Ip Wai Hung, Yung Kai Leung, L. Zeng
Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.
{"title":"Support vector regression model for flight demand forecasting","authors":"Wei-gang Fan, Xiang Wu, Xin Yang Shi, Chong Zhang, Ip Wai Hung, Yung Kai Leung, L. Zeng","doi":"10.1177/18479790231174318","DOIUrl":"https://doi.org/10.1177/18479790231174318","url":null,"abstract":"Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78001329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231165603
Derrick W. H. Fung
This study presents a teaching case that analyzes the applicability of the Z-Score bankruptcy prediction model to manufacturing firms listed in Hong Kong. Although the Z-Score model has been studied extensively, there are very few studies in the context of the Hong Kong stock market. Given that the Hong Kong stock market has high retail investor participation and low liquidity, whether the Z-Score model is relevant to Hong Kong investors is an important but unanswered question. The Z-Score model predicts the bankruptcy of firms by considering financial ratios involving firm liquidity, solvency, profitability, leverage, and activity. Financial and stock return data on the manufacturing firms listed in the Hong Kong Stock Exchange from 1981 to 2020 are collected from Thomson Reuters Datastream to examine the applicability of the Z-Score model in Hong Kong. Firms are then classified into bankrupt or non-bankrupt groups based on their Z-Scores. The annual stock returns in the subsequent year are analyzed for the two groups after classification. When the Z-Score threshold is set at 0, investing in the non-bankrupt group and short-selling the bankrupt group earns an annual return of 11.99% in the subsequent year. The results are robust to alternative periods and lagged values of the Z-Score. This suggests that stock prices do not reflect all the accounting data and that investors can increase their returns using the Z-Score model. As retail investors have limited resources, it may be difficult for them to fully implement the Z-Score model for a portfolio that consists of thousands of stocks. However, they can still avoid substantial losses by not investing in firms with low Z-Scores.
{"title":"Identifying poorly performing listed firms using data analytics","authors":"Derrick W. H. Fung","doi":"10.1177/18479790231165603","DOIUrl":"https://doi.org/10.1177/18479790231165603","url":null,"abstract":"This study presents a teaching case that analyzes the applicability of the Z-Score bankruptcy prediction model to manufacturing firms listed in Hong Kong. Although the Z-Score model has been studied extensively, there are very few studies in the context of the Hong Kong stock market. Given that the Hong Kong stock market has high retail investor participation and low liquidity, whether the Z-Score model is relevant to Hong Kong investors is an important but unanswered question. The Z-Score model predicts the bankruptcy of firms by considering financial ratios involving firm liquidity, solvency, profitability, leverage, and activity. Financial and stock return data on the manufacturing firms listed in the Hong Kong Stock Exchange from 1981 to 2020 are collected from Thomson Reuters Datastream to examine the applicability of the Z-Score model in Hong Kong. Firms are then classified into bankrupt or non-bankrupt groups based on their Z-Scores. The annual stock returns in the subsequent year are analyzed for the two groups after classification. When the Z-Score threshold is set at 0, investing in the non-bankrupt group and short-selling the bankrupt group earns an annual return of 11.99% in the subsequent year. The results are robust to alternative periods and lagged values of the Z-Score. This suggests that stock prices do not reflect all the accounting data and that investors can increase their returns using the Z-Score model. As retail investors have limited resources, it may be difficult for them to fully implement the Z-Score model for a portfolio that consists of thousands of stocks. However, they can still avoid substantial losses by not investing in firms with low Z-Scores.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87991291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231188548
Sebastiano Di Luozzo, Fiorenza Starnoni, M. Schiraldi
In the industrial field, one of the most widespread KPIs is represented by the Overall Equipment Effectiveness (OEE), first introduced by Seiichi Nakajima within the Total Productive Maintenance (TPM) theory and aimed at identifying the inefficiencies of industrial assets. While OEE has been objective of several studies, the relationship between the Overall Equipment Effectiveness and the role of the human factor in achieving its high levels of values has not been extensively investigated. In recent years few scientific studies have investigated the relationship, showing that there is a link between OEE and human factors, even significant, but not clearly identified yet. In order to examine this relationship, our study proposes a framework to clarify the links between human factors, OEE parameters, the industrial sector, and the degree of automation. This framework is then validated through the application of the Analytic Hierarchy Process (AHP) methodology. As a result, 13 aspects related to the human factor were identified. Finally, the study provides practical guidance and implications for maximizing the outcomes of the investigation, with the goal of improving an organization’s overall manufacturing performance. By understanding the impact of the human factor on OEE, organizations can make informed decisions to optimize their operations and achieve higher levels of productivity.
{"title":"On the relationship between human factor and overall equipment effectiveness (OEE): An analysis through the adoption of analytic hierarchy process and ISO 22400","authors":"Sebastiano Di Luozzo, Fiorenza Starnoni, M. Schiraldi","doi":"10.1177/18479790231188548","DOIUrl":"https://doi.org/10.1177/18479790231188548","url":null,"abstract":"In the industrial field, one of the most widespread KPIs is represented by the Overall Equipment Effectiveness (OEE), first introduced by Seiichi Nakajima within the Total Productive Maintenance (TPM) theory and aimed at identifying the inefficiencies of industrial assets. While OEE has been objective of several studies, the relationship between the Overall Equipment Effectiveness and the role of the human factor in achieving its high levels of values has not been extensively investigated. In recent years few scientific studies have investigated the relationship, showing that there is a link between OEE and human factors, even significant, but not clearly identified yet. In order to examine this relationship, our study proposes a framework to clarify the links between human factors, OEE parameters, the industrial sector, and the degree of automation. This framework is then validated through the application of the Analytic Hierarchy Process (AHP) methodology. As a result, 13 aspects related to the human factor were identified. Finally, the study provides practical guidance and implications for maximizing the outcomes of the investigation, with the goal of improving an organization’s overall manufacturing performance. By understanding the impact of the human factor on OEE, organizations can make informed decisions to optimize their operations and achieve higher levels of productivity.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82942999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231160857
H. Lam, Valerie Tang
During the pandemic, the attention and demand for cold chain increased owing to considerable use of low-temperature logistics in transporting perishable goods and vaccines. To ensure the shipping performance for reduced damage, logistics companies are required to track continually and repetitively the status of shipments daily. However, typing various air waybills for searching the shipping status is a cause of frequent errors. Also, tracking the shipping status is labor-intensive, resource intensive, inefficient and repetitive. Moreover, repetitive tasks result in low employee satisfaction. Therefore, robotic process automation (RPA) applications have gained the attention of practitioners in the cold chain logistics industry. This study contributes to (i) determining possible areas requiring automation through the workflow study on cold chain logistics and (ii) streamlining the operation by the develop a robotic process automation bots. A case study tested and evaluated the performance of two unattended RPA bots applied in a freight forwarder company to check shipment status and temperature conditions. The results determined that implementing RPA in the workflow reduces significant data processing time. With the implementation of proposed RPA bots, the company can better comprehend its shipping performance of logistics and can get an immediate notification from RPA bots when an abnormal situation occurs with regard to a shipment.
{"title":"Digital transformation for cold chain management in freight forwarding industry","authors":"H. Lam, Valerie Tang","doi":"10.1177/18479790231160857","DOIUrl":"https://doi.org/10.1177/18479790231160857","url":null,"abstract":"During the pandemic, the attention and demand for cold chain increased owing to considerable use of low-temperature logistics in transporting perishable goods and vaccines. To ensure the shipping performance for reduced damage, logistics companies are required to track continually and repetitively the status of shipments daily. However, typing various air waybills for searching the shipping status is a cause of frequent errors. Also, tracking the shipping status is labor-intensive, resource intensive, inefficient and repetitive. Moreover, repetitive tasks result in low employee satisfaction. Therefore, robotic process automation (RPA) applications have gained the attention of practitioners in the cold chain logistics industry. This study contributes to (i) determining possible areas requiring automation through the workflow study on cold chain logistics and (ii) streamlining the operation by the develop a robotic process automation bots. A case study tested and evaluated the performance of two unattended RPA bots applied in a freight forwarder company to check shipment status and temperature conditions. The results determined that implementing RPA in the workflow reduces significant data processing time. With the implementation of proposed RPA bots, the company can better comprehend its shipping performance of logistics and can get an immediate notification from RPA bots when an abnormal situation occurs with regard to a shipment.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77406852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231206019
Heba Allah Samir, Laila Abd-Elmegid, Mohamed Marie
Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.
{"title":"Sentiment analysis model for Airline customers’ feedback using deep learning techniques","authors":"Heba Allah Samir, Laila Abd-Elmegid, Mohamed Marie","doi":"10.1177/18479790231206019","DOIUrl":"https://doi.org/10.1177/18479790231206019","url":null,"abstract":"Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135212390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/18479790231172874
Tahereh Hasani, Davar Rezania, Nadège Levallet, Norman O’Reilly, Mohammad Mohammadi
As society places greater emphasis on information privacy and data protection, organizations are increasingly adopting Privacy Enhancing Technologies (PETs) to safeguard the personal information of their stakeholders. This trend is fueled by growing consumer awareness and the introduction of government regulations aimed at protecting personal data. By implementing PETs, organizations can ensure compliance with privacy regulations and establish trust with their customers. This study aims to deepen the understanding of the determinants of Privacy Enhancing Technology (PET) adoption in small and medium-sized enterprises (SMEs) and its impact on their performance. It focuses on the technology-organization-environment (TOE) model, managerial readiness, firm size, industry sector, and intent to adopt PETs as potential drivers of PET adoption. By using a large-scale survey of 202 Canadian SMEs, the study evaluates the mediating role of intent in the relationship between the TOE model, managerial readiness, and market performance. The results of this study contribute to the growing body of research on PET adoption in SMEs and provide insights for organizations and managers to effectively adopt PETs. The results of this study indicate that technological, environmental, organizational, and managerial readiness have a positive effect on the intention to adopt PETs. Additionally, the intention to adopt PETs was found to have a positive relationship with firm performance. The findings also reveal that the intention to adopt PETs fully mediates the relationship between the four dimensions of readiness and firm performance. These findings highlight the important role that readiness and intention play in the adoption of PETs and its impact on firm performance. This study also found that firm size moderates the relationship between technological and organizational readiness with intention to adopt PETs, as well as the relationship between environmental and managerial readiness with intention to adopt PETs. The study identified the top five factors affecting PET adoption as cybersecurity awareness, perceived cost of adoption, ease of use, perceived benefits, and IT infrastructure. The findings suggest that technological readiness is the most influential of the four dimensions, followed by organizational, environmental, and managerial factors. This study presents crucial considerations for SMEs to evaluate when deciding on the use of PET technologies, as it pertains to practitioners.
{"title":"Privacy enhancing technology adoption and its impact on SMEs’ performance","authors":"Tahereh Hasani, Davar Rezania, Nadège Levallet, Norman O’Reilly, Mohammad Mohammadi","doi":"10.1177/18479790231172874","DOIUrl":"https://doi.org/10.1177/18479790231172874","url":null,"abstract":"As society places greater emphasis on information privacy and data protection, organizations are increasingly adopting Privacy Enhancing Technologies (PETs) to safeguard the personal information of their stakeholders. This trend is fueled by growing consumer awareness and the introduction of government regulations aimed at protecting personal data. By implementing PETs, organizations can ensure compliance with privacy regulations and establish trust with their customers. This study aims to deepen the understanding of the determinants of Privacy Enhancing Technology (PET) adoption in small and medium-sized enterprises (SMEs) and its impact on their performance. It focuses on the technology-organization-environment (TOE) model, managerial readiness, firm size, industry sector, and intent to adopt PETs as potential drivers of PET adoption. By using a large-scale survey of 202 Canadian SMEs, the study evaluates the mediating role of intent in the relationship between the TOE model, managerial readiness, and market performance. The results of this study contribute to the growing body of research on PET adoption in SMEs and provide insights for organizations and managers to effectively adopt PETs. The results of this study indicate that technological, environmental, organizational, and managerial readiness have a positive effect on the intention to adopt PETs. Additionally, the intention to adopt PETs was found to have a positive relationship with firm performance. The findings also reveal that the intention to adopt PETs fully mediates the relationship between the four dimensions of readiness and firm performance. These findings highlight the important role that readiness and intention play in the adoption of PETs and its impact on firm performance. This study also found that firm size moderates the relationship between technological and organizational readiness with intention to adopt PETs, as well as the relationship between environmental and managerial readiness with intention to adopt PETs. The study identified the top five factors affecting PET adoption as cybersecurity awareness, perceived cost of adoption, ease of use, perceived benefits, and IT infrastructure. The findings suggest that technological readiness is the most influential of the four dimensions, followed by organizational, environmental, and managerial factors. This study presents crucial considerations for SMEs to evaluate when deciding on the use of PET technologies, as it pertains to practitioners.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90661843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1177/18479790221113621
Pornrat Sadangharn
This study aims to develop a model for the acceptance of robots as co-workers from the perspective of hotel employees and uses empirical model testing to validate the findings. Mixed-methods research was conducted by employing a sequential exploratory strategy, whereas qualitative research was conducted using interpretative phenomenological analysis (IPA). The key informants were executives, HR managers, reception managers, and some staff of three hotels in Thailand. Five main themes were uncovered from the IPA: human, robot, organization, human–robot collaboration (HRC), and robot acceptance. Relationships between the themes were established and were promoted as the premise for an initial robot acceptance model. Thereafter, the survey questionnaire was drafted using the instrumental development approach. The model is a good fit with the empirical data. Human, robot, and organizational factors significantly affect robot acceptance and HRC. Meanwhile, HRC plays a mediator role in the relationship of human, robot, and organizational factors with robot acceptance, but in a negative direction. This implies that the respondents generally accept robots. However, the level of acceptance decreases when HRC is involved.
{"title":"Acceptance of robots as co-workers: Hotel employees’ perspective","authors":"Pornrat Sadangharn","doi":"10.1177/18479790221113621","DOIUrl":"https://doi.org/10.1177/18479790221113621","url":null,"abstract":"This study aims to develop a model for the acceptance of robots as co-workers from the perspective of hotel employees and uses empirical model testing to validate the findings. Mixed-methods research was conducted by employing a sequential exploratory strategy, whereas qualitative research was conducted using interpretative phenomenological analysis (IPA). The key informants were executives, HR managers, reception managers, and some staff of three hotels in Thailand. Five main themes were uncovered from the IPA: human, robot, organization, human–robot collaboration (HRC), and robot acceptance. Relationships between the themes were established and were promoted as the premise for an initial robot acceptance model. Thereafter, the survey questionnaire was drafted using the instrumental development approach. The model is a good fit with the empirical data. Human, robot, and organizational factors significantly affect robot acceptance and HRC. Meanwhile, HRC plays a mediator role in the relationship of human, robot, and organizational factors with robot acceptance, but in a negative direction. This implies that the respondents generally accept robots. However, the level of acceptance decreases when HRC is involved.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91047459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-07DOI: 10.1177/18479790221095641
Sukathong S, Suksawang P and Naenna T. Analyzing the importance of critical success factors for the adoption of advanced manufacturing technologies. International Journal of Engineering Business Management 2021; 13: 1–16. DOI: 10.1177/18479790211055057
{"title":"Correction notice to “Analyzing the importance of critical success factors for the adoption of advanced manufacturing technologies”","authors":"","doi":"10.1177/18479790221095641","DOIUrl":"https://doi.org/10.1177/18479790221095641","url":null,"abstract":"Sukathong S, Suksawang P and Naenna T. Analyzing the importance of critical success factors for the adoption of advanced manufacturing technologies. <i>International Journal of Engineering Business Management</i> 2021; 13: 1–16. DOI: 10.1177/18479790211055057","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-27DOI: 10.1177/18479790211067345
Su-Ju Lu, Yu-Chiao Lin, K. Tan, Ying-Chieh Liu
In light of a recent spike in natural and man-made disasters, there has been an increase in interest in disaster prevention education and training. The effectiveness of both publicly-funded and voluntarily organized disaster education (DE) has attracted wide attention. More studies are needed to understand the innovative pedagogical practice and the impact of technological advances on disaster learning content development, effectiveness and motivation. This study investigates the application of augmented reality (AR) in DE and training. An AR-enhanced tool named ‘disaster-proof warrior’ was developed and tested to evaluate its enhancement effect on learning under two collaborative learning modes. A series of quasi-experiments involving 85 elementary school subjects was carried out to assess the learning effectiveness and the subjective reaction in learning motivation. The results showed the AR embedded learning tool is effective in engaging and motivating collaborative team knowledge building. This study adds to the existing literature of AR applications in education and training as well as providing a useful reference for future development and improvement of national DE and training.
{"title":"Revolutionizing elementary disaster prevention education and training via augmented reality-enhanced collaborative learning","authors":"Su-Ju Lu, Yu-Chiao Lin, K. Tan, Ying-Chieh Liu","doi":"10.1177/18479790211067345","DOIUrl":"https://doi.org/10.1177/18479790211067345","url":null,"abstract":"In light of a recent spike in natural and man-made disasters, there has been an increase in interest in disaster prevention education and training. The effectiveness of both publicly-funded and voluntarily organized disaster education (DE) has attracted wide attention. More studies are needed to understand the innovative pedagogical practice and the impact of technological advances on disaster learning content development, effectiveness and motivation. This study investigates the application of augmented reality (AR) in DE and training. An AR-enhanced tool named ‘disaster-proof warrior’ was developed and tested to evaluate its enhancement effect on learning under two collaborative learning modes. A series of quasi-experiments involving 85 elementary school subjects was carried out to assess the learning effectiveness and the subjective reaction in learning motivation. The results showed the AR embedded learning tool is effective in engaging and motivating collaborative team knowledge building. This study adds to the existing literature of AR applications in education and training as well as providing a useful reference for future development and improvement of national DE and training.","PeriodicalId":45882,"journal":{"name":"International Journal of Engineering Business Management","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81554339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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