A good company gone bad

2019 ◽  
Vol 23 (1) ◽  
pp. 31-51 ◽  
Author(s):  
Young Eun Park ◽  
Hyunsang Son ◽  
Sung-Un Yang ◽  
Jae Kook Lee
Keyword(s):  
Machine Learning ◽  
Public Relations ◽  
News Media ◽  
Machine Learning Techniques ◽  
Agenda Building ◽  
Press Releases ◽  
Content Type ◽  
Corporate Social ◽  
Corporate Crisis ◽  
Crisis Situations

PurposeThe purpose of this paper is to demonstrate whether or not public relations efforts in corporate social responsibility (CSR) influence the news media in corporate crisis situations.Design/methodology/approachThe study conducted a content analysis of press releases and news media based on traditional human-coded cross-lag analyses and a machine learning technique, a novel method of big data analysis to test hypotheses.FindingsResults indicate that CSR press releases indeed influenced the news media. During the crisis point, however, agenda-building was not observed.Practical implicationsCorporations need to continue CSR activities and provide public relations materials consistently even after a crisis, as an agenda-building role could be recovered.Originality/valueThe study examines the relationship between CSR and crisis situations in an agenda-building theoretical framework. The authors introduce agenda-building in the corporate sector with machine learning techniques.

Share of voices in corporate social responsibility (CSR) news

2019 ◽  
Vol 24 (1) ◽  
pp. 128-142
Author(s):  
Lisa Tam
Keyword(s):  
News Media ◽  
Design Methodology ◽  
News Coverage ◽  
Press Releases ◽  
Content Type ◽  
The Press ◽  
Negative Comments ◽  
Building Effects ◽  
Corporate Social ◽  
Different Sources

Purpose The use of sources in news coverage affects news audience’s perceptions of news events. To extend existing research on inter media agenda-setting and agenda-building effects of CSR-related news, the purpose of this paper is to explore the representation and share of voices in CSR-related news by investigating and comparing the use of sources in press releases and news coverage. Design/methodology/approach This study content-analyzed the 202 CSR-related press releases published by the two electricity providers in Hong Kong and 1,045 news articles related to the press releases over a five-year period. A total of 402 quotes from the press releases and 1,880 quotes from the news coverage were analyzed, including the types of sources cited, the tone of the sources and variations in the use of sources across seven different CSR themes. Findings Although company representatives were quoted the most in both the press releases and news coverage, NGOs, government representatives and industry analysts were the most frequently cited for negative comments in the news coverage. Differences were found between the press releases and news coverage in terms of how frequently different sources were cited, the tone attributed to those sources, and the choice of sources across different CSR themes. Originality/value The findings reflect that corporations are not necessarily the most influential voice in CSR and that other groups also have their views represented in the news media. The representation of these voices differed by CSR themes. Corporations are advised to further explore what and how different voices are represented in the news coverage in relation to their CSR activities and to consider these voices when making decisions about CSR.

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Different firm responses to the COVID-19 pandemic shocks: machine-learning evidence on the Vietnamese labor market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lam Hoang Viet Le ◽  
Toan Luu Duc Huynh ◽  
Bryan S. Weber ◽  
Bao Khac Quoc Nguyen
Keyword(s):  
Machine Learning ◽  
Labor Market ◽  
Large Scale ◽  
Government Support ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Firm Characteristics ◽  
Data Set ◽  
Content Type ◽  
Firm Responses

PurposeThis paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.Design/methodology/approachThe authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.FindingsFirst, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.Originality/valueThis study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.

Understand corporate social responsibility from an agenda setting perspective: a cross-national analysis of newspaper using computer-assisted content analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yafei Zhang ◽  
Chuqing Dong
Keyword(s):  
Corporate Social Responsibility ◽  
Content Analysis ◽  
Social Responsibility ◽  
News Media ◽  
Agenda Setting ◽  
Computer Assisted ◽  
Content Type ◽  
News Sources ◽  
National Analysis ◽  
Corporate Social

Purpose This study aims to explore multifaceted corporate social responsibility (CSR) covered in popular English newspapers in the UK, USA, mainland China and Hong Kong from 2000 to 2016 via a computer-assisted analytical approach. This study moves the understanding of CSR away from corporate self-reporting to the mass media and raises interesting questions about the role of the news media in presenting CSR as a multifaceted, socially constructed concept. Design/methodology/approach Data were retrieved from CSR-related news articles from 2000 to 2016 that were archived in the LexisNexis database. Guided by the theoretical framework of agenda setting, a computer-assisted content analysis (Latent Dirichlet Allocation) was used to analyze 4,487 CSR-related articles from both business and non-business news sources. Analysis of variance was used to compare salient CSR topics in each country/region. Findings This study identifies newspapers as an alternate to corporations’ attempts to distribute CSR information and construct CSR meaning. The findings revealed that the news communicates a variety of CSR issues that are aligned or beyond what CSR was defined in corporate CSR reporting, as suggested in previous studies. In addition, CSR news coverages differ between the business and nonbusiness news sources. Furthermore, the media tone of CSR coverage significantly differed across the regions and between the business and nonbusiness newspapers. Social implications Emerging topics in CSR news coverage, such as business education, could help companies identify untapped CSR realms in the market. Originality/value This study contributes to CSR communication research by adding a non-corporate perspective regarding what CSR means and should be focused on. The news media presents CSR using a heterogeneous approach as they not only provide surface reports on corporations’ CSR activities but also offer in-depth discussions.

How can companies succeed in forming CSR reputation?

2016 ◽  
Vol 21 (4) ◽  
pp. 435-449 ◽  
Author(s):  
Sun Young Lee
Keyword(s):  
Design Methodology ◽  
Secondary Analysis ◽  
Specific Aspect ◽  
Press Releases ◽  
Primary Methods ◽  
Content Type ◽  
Corporate Websites ◽  
Corporate Social ◽  
Facebook Pages ◽  
Existing Data

Purpose The purpose of this paper is to explore the channels companies use to communicate their corporate social responsibility (CSR) messages and to test the effectiveness of those channels – specifically, press releases, corporate websites, CSR reports, corporate Facebook pages, and TV advertising – on forming companies’ CSR reputations. Design/methodology/approach The two primary methods used in this study were secondary analysis of existing data and content analysis. The study sample was the 101 companies in the Reputation Institute’s 2014 CSR ranking of the 100 most highly regarded companies (two companies were tied) across 15 countries. Findings Corporate websites and CSR reports were the most common channels for CSR communications, but press releases – through their impact on news articles – and general corporate Facebook pages were the only effective channels in forming CSR reputation. Originality/value This study provides empirical evidence of the effectiveness of various CSR communication channels; it not only focuses on CSR reputation, a specific aspect of corporate reputation which has not been studied in this context before, but also examines several different channels simultaneously, in contrast to previous studies which have only investigated one or two channels at a time.

scholarly journals Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

2019 ◽  
Vol 119 (3) ◽  
pp. 676-696 ◽  
Author(s):  
Zhongyi Hu ◽  
Raymond Chiong ◽  
Ilung Pranata ◽  
Yukun Bao ◽  
Yuqing Lin
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Performance Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
Domain Identification ◽  
Learning Techniques ◽  
And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

Pervasive computing in the context of COVID-19 prediction with AI-based algorithms

2020 ◽  
Vol 16 (5) ◽  
pp. 477-487 ◽  
Author(s):  
Magesh S. ◽  
Niveditha V.R. ◽  
Rajakumar P.S. ◽  
Radha RamMohan S. ◽  
Natrayan L.
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Pervasive Computing ◽  
Research Work ◽  
Previous History ◽  
Physical Contact ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Susceptible Population ◽  
Content Type

Purpose The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters. Design/methodology/approach For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease. Findings Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history. Originality/value The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

scholarly journals Elusive corporate social responsibility (CSR) in global shipping

2015 ◽  
Vol 6 (1) ◽  
pp. 80-98 ◽  
Author(s):  
Helen Sampson ◽  
Neil Ellis
Keyword(s):  
Corporate Social Responsibility ◽  
Public Relations ◽  
Social Responsibility ◽  
Living Conditions ◽  
The Body ◽  
Shipping Industry ◽  
Content Type ◽  
Body Of Knowledge ◽  
Normative Basis ◽  
Corporate Social

Purpose – This paper aims to, using the example of the highly globalised shipping industry, shed light upon the practice of corporate social responsibility (CSR) and the extent to which it might be relied upon to fill international regulatory gaps. Design/methodology/approach – The paper draws upon findings from a questionnaire study of shipboard accommodation. Findings – The paper finds that seafarers’ welfare remains under-considered by many companies. It suggests that the consolidation of regulation pertaining to seafarer living conditions under the Maritime Labour Convention (MLC) has been timely. However, a priority for the international community should be to develop the relatively low standards currently required by existing regulation to provide for better standards of seafarer welfare across the global fleet. Research limitations/implications – This evidence from the shipping industry challenges arguments for the normative basis for CSR and lends weight to those suggesting that the apparent exercise of CSR by multinational companies should broadly be understood as an exercise in public relations. Social implications – The research points to the need for the MLC to be amended to raise the mandatory standards of shipboard accommodation in the merchant shipping industry. Originality/value – The paper contributes unique data on seafarers’ living conditions and augments the body of knowledge concerning the exercise of CSR in global sectors.

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