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Article
Publication date: 28 March 2023

Hakan Erkutlu, Jamel Chafra, Hatice Ucak and Rahsan Kolutek

This paper aims to investigate the relationship between emotional labor and workplace violence based on the social exchange theory. Drawing on the social exchange theory, this…

Abstract

Purpose

This paper aims to investigate the relationship between emotional labor and workplace violence based on the social exchange theory. Drawing on the social exchange theory, this paper aims to investigate the relationship between emotional labor and workplace violence. Specifically, the authors take a relational approach by introducing positive patient treatment as the mediator. The moderating role of organizational support in the relationship between emotional labor and workplace violence is also considered.

Design/methodology/approach

The data of this study encompasses 536 nurses from 10 university hospitals in Turkey. Hierarchical multiple regression analysis was conducted to test the proposed model.

Findings

The findings of this study support the negative effect of emotional labor on workplace violence and the mediating effect of patient-positive treatment. Moreover, when organizational support is low, the relationship between emotional labor and workplace violence is strong. In contrast, the effect is weak when organizational support is high.

Practical implications

The findings of this study suggest that health-care administrators should offer more training to nurses to help them manage their emotions while interacting with their patients. This leads to positive interpersonal relationships, which, in turn, lowers workplace violence. Moreover, health-care administrators should pay more attention to the buffering role of perceived organizational support for those subordinates with low emotional labor and higher workplace violence.

Originality/value

The study provides new insights into emotional labor’s influence on workplace violence and the moderating role of organizational support in the link between emotional labor and workplace violence. The paper also offers practical assistance to nurses in the health-care industry interested in building positive patient treatment and trust with their patients and minimizing workplace violence.

Details

Journal of Aggression, Conflict and Peace Research, vol. 16 no. 1
Type: Research Article
ISSN: 1759-6599

Keywords

Article
Publication date: 18 May 2023

Harry P. Bowen and Leo Sleuwaegen

This paper aims to derive and estimate a theory-based empirical specification that models a firm’s choices of its international diversification (ID) and product diversification…

Abstract

Purpose

This paper aims to derive and estimate a theory-based empirical specification that models a firm’s choices of its international diversification (ID) and product diversification (PD) and how they evolve over time in response to shocks that alter the relative cost and relative profitability of ID and PD.

Design/methodology/approach

We use longitudinal data on U.S. manufacturing firms from 1984 to 1999, a period of intense shocks associated with rapid globalization, to estimate a dynamic panel data Tobit model that permits lags in a firm’s adjustment to its optimal mix of ID and PD over time.

Findings

We find strong support for the theoretical framework underlying our empirical specifications and posited dynamics, with full adjustment estimated to require, on average, 1.5 years, a finding with implications for the time spacing of observations in empirical studies of ID and PD to avoid biased inferences. Among the globalization shocks during the time period studied, our results indicate that global competitive pressures and efficiency gains from global supply integration to be the more important factors driving U.S. firms toward greater ID relative to PD. Augmentation of firms’ organizational (managerial) and physical capital resources is also found to be important for supporting an expansion of ID relative to PD. Technological resource augmentation is instead found to favor expansion of PD relative to ID.

Originality/value

Our empirical specification is novel. It readily incorporates an often ignored but necessary theoretical condition that defines a firm’s optimal choices of its ID and PD, and it allows observed choices at a point in time to deviate from their optimal values.

Details

Review of International Business and Strategy, vol. 33 no. 5
Type: Research Article
ISSN: 2059-6014

Keywords

Article
Publication date: 7 August 2023

Deepika Jhamb, Sukhpreet Kaur, Saurabh Pandey and Amit Mittal

Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The…

Abstract

Purpose

Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.

Design/methodology/approach

Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.

Findings

The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.

Originality/value

By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 26 March 2024

Rohit Kumar Singh and Sachin Modgil

The main aim of this study is to explore the relationship between information system flexibility and dynamic capabilities to build sustainable and net zero supply chains under the…

Abstract

Purpose

The main aim of this study is to explore the relationship between information system flexibility and dynamic capabilities to build sustainable and net zero supply chains under the influence of environmental dynamism.

Design/methodology/approach

We have formulated a self-administered survey, with 359 participants contributing responses. Prior to delving into foundational assumptions, such as homoscedasticity and normality, a nonresponse bias analysis was executed. The integrity of the data, in terms of reliability and construct validity, was gauged using confirmatory factor analysis. Subsequent regression outputs corroborated all the proposed assumptions, fortifying the extant scholarly literature.

Findings

The empirical findings of this research underscore a positive correlation between Information system flexibility, dynamic capabilities and a net zero supply chain, especially in the context of environmental dynamism. Data sourced from the cement manufacturing sector support these observations. We also found that environmental dynamism moderates the relationship between data analytics capability and sustainable supply chain flexibility but does not moderate the relationship between Resource flexibility and sustainable supply chain flexibility. Additionally, this research strengthens the foundational principles of the dynamic capability theory.

Originality/value

The conceptual framework elucidates the interplay between information system flexibility, dynamic capabilities, and sustainable supply chain flexibility, emphasizing their collective contribution towards achieving sustainable chain net zero, introducing environmental dynamics as a moderating variable that augments the scholarly discourse with a nuanced layer of analytical depth.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 28 October 2022

Ashutosh Samadhiya, Rajat Agrawal, Sunil Luthra, Anil Kumar, Jose Arturo Garza-Reyes and Deepak Kumar Srivastava

The purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a…

Abstract

Purpose

The purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a Circular Economy (CE). Also, the paper explores the combined impact of TPM, I4.0 and CE on the sustainability performance (SP) of manufacturing firms.

Design/methodology/approach

The conceptual model is proposed using the dynamic capability view (DCV) and empirically validated by partial least squares-structural equation modelling (PLS-SEM) using 304 responses from Indian manufacturing firms.

Findings

The results suggest that I4.0 positively impacts TPM, CE and SP, also showing TPM's positive impact on CE and SP. In addition, CE has a positive influence on the SP of manufacturing firms. Furthermore, CE partially mediates the relationship between I4.0 and SP with TPM and SP. The study also identifies TPM, I4.0 and CE as a new bundle of dynamic capabilities to deliver SP in manufacturing firms.

Originality/value

The present research adds to the knowledge and literature on DCV by identifying the importance of CE in the settings of I4.0 and TPM, especially in the context of sustainability. Also, the current study offers a new set of dynamic capabilities and provides some significant future recommendations for researchers and practitioners.

Details

The International Journal of Logistics Management, vol. 34 no. 3
Type: Research Article
ISSN: 0957-4093

Keywords

Article
Publication date: 19 September 2023

Amit Kumar, Som Sekhar Bhattacharyya and Bala Krishnamoorthy

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in…

Abstract

Purpose

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

Design/methodology/approach

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

Findings

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

Research limitations/implications

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Practical implications

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Social implications

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

Originality/value

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

Details

Journal of Enterprise Information Management, vol. 36 no. 6
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 30 January 2024

Eunsuk Hong, Jong-Kook Shin and Huan Zou

Extending the springboard perspective with the resource dependence theory, the authors posit that cross-border mergers and acquisitions (M&As) are a new channel for emerging…

Abstract

Purpose

Extending the springboard perspective with the resource dependence theory, the authors posit that cross-border mergers and acquisitions (M&As) are a new channel for emerging economy firms (EEFs) to enhance their technology capabilities. This study aims to examine the impact of cross-border M&As initiated by EEFs on their technology augmentation vis-à-vis matched domestic M&A cases and investigate the factors influencing the difference in post-merger innovation capability.

Design/methodology/approach

This paper estimates the post-acquisition innovation capability of acquirers from emerging economies (EEs) that engage in cross-border M&As. To remove possible selection bias, the authors leverage a difference-in-difference-style approach in combination with a matched sample constructed by pairing each cross-border M&A case with a similar domestic deal. The data set contains 266 cross-border M&As and 266 matched domestic M&A deals between 2003 and 2011, whereby acquirers are based in 6 EEs and targets are in 36 countries consisting of both EEs and advanced economies (AEs).

Findings

The present empirical results show that cross-border M&As engaged by EEFs are an important engine for improving EEFs’ innovation capability through technology augmentation. The main empirical results are as follows. First, compared with matched domestic acquirers with similar characteristics, EE cross-border M&As have a positive effect on innovation capability. Second, the positive effect of the EEFs’ cross-border M&As relative to the matched domestic M&As on innovation capability is driven largely by cross-border M&As with targets in AEs. Third, the increase in post-M&A innovation capability of the EE cross-border acquirers comes mainly from deals where targets are based in countries with relatively superior human capital and innovation capability than those of the acquirers.

Originality/value

To the best of the authors’ knowledge, this study is the first systematic study of whether cross-border M&As serve as an effective channel of technology augmentation for EE acquirers compared to matched domestic acquirers with similar characteristics.

Details

International Journal of Development Issues, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1446-8956

Keywords

Article
Publication date: 9 August 2023

Ziyan Guo, Xuhao Liu, Zehua Pan, Yexin Zhou, Zheng Zhong and Zilin Yan

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic…

Abstract

Purpose

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Design/methodology/approach

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

Findings

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

Originality/value

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

Details

Engineering Computations, vol. 40 no. 7/8
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 17 May 2022

María Belén Ortiz and Stanislav Karapetrovic

Augmentation of an ISO 10001 code system for healthcare worker (HW) satisfaction with ISO/IEC 27701 and ISO/IEC 29184 privacy-related subsystems is shown. Four specific codes…

165

Abstract

Purpose

Augmentation of an ISO 10001 code system for healthcare worker (HW) satisfaction with ISO/IEC 27701 and ISO/IEC 29184 privacy-related subsystems is shown. Four specific codes regarding the privacy of HWs using electronic devices for hand hygiene (HH) monitoring and the related activities are presented.

Design/methodology/approach

HWs’ concerns involving automated hand hygiene monitoring technologies were identified through a literature review and classified. Privacy codes (PCs) that deal with such concerns were developed. ISO/IEC 27701 requirements for privacy information were mapped to the elements of these codes, labelled as “Healthcare Workers’ Hand Hygiene Privacy Codes (HW-HH-PCs)”. Both ISO/IEC 27701 and ISO/IEC 29184 guidelines for Privacy Notices and consent were linked with the activities for preparing the code resources.

Findings

Components of an ISO/IEC 27701 system, the guidance of ISO/IEC 29184 and the definitions provided in ISO/IEC 29100 can assist the preparation of HW-HH-PCs and the required resources. An ISO/IEC 29184 Privacy Notice can be used as input for developing an Informed Consent Form, which can be implemented to suit two of the four developed HW-HH-PCs.

Practical implications

HW-HH-PCs and the supporting resources, which healthcare organizations could implement to potentially increase quality assurance of an automated HH monitoring service, are illustrated.

Originality/value

Integrative augmentation of ISO 10001:2018, ISO/IEC 27701:2019 and ISO/IEC 29184:2020 within an underlying framework from ISO/IEC 20000–1:2018 for information technology service, together with the related examples of privacy-related customer satisfaction codes and the corresponding resources, is introduced.

Details

The TQM Journal, vol. 35 no. 5
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 13 December 2022

Chengxi Yan, Xuemei Tang, Hao Yang and Jun Wang

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the…

Abstract

Purpose

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

Design/methodology/approach

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Findings

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

Originality/value

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

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