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This study investigates the mechanism through which banks employ corporate social responsibility (CSR) commitment to engage in employees. The values of different types of CSR engagement (i.e. philanthropic CSR vs ethical and legal CSR) are distinguished and their influences on employee identification are analyzed. The moderation effect of CSR communication through corporate social media is examined in this context.

A sample of 254 respondents was collected through surveying the employees of one of the largest banks in Turkey.

Findings suggest that ethical and legal CSR is perceived more importantly than philanthropic CSR by employees in the banking industry. In addition, the level of transparency and frequency of CSR communication through corporate social media moderates the CSR types–employee identification relationship distinctively.

Special attention should be paid to the conditions under which CSR communication takes place effectively, as skeptics toward certain types of CSR initiatives may occur along with the disclosure of information about them.

If organizations use social media communication in a way that would bring the CSR interests of their employees to light, it is likely that CSR initiatives will become more meaningful and have a greater societal impact.

This study contributes to the CSR literature through identifying the value of different types of CSR initiative and confirming the importance of transparent and proactive CSR communication on employee identification in the banking sector.

Corporate-NGO partnerships are gaining increasing importance as part of a company's CSR effort. This study aims to understand which communication tactics (CSR motive, CSR message frame, CSR fit) lead to more positive consumer outcomes in the context of corporate-NGO partnerships, and whether consumer skepticism and consumer trust mediate the proposed relationships.

An online experiment was conducted (N = 298) to examine the theoretical predictions, involving a 2 (CSR motive: firm-serving/public-serving) x 2 (CSR message frame: narrative/expositive) x 2 (CSR fit: high/low) between-subjects design.

The results confirmed that consumer attitudes and electronic Word-of-Mouth (eWOM) can be affected by CSR motives and CSR fit. Also, CSR skepticism and consumer trust both mediate the relationship of CSR motives and consumer outcomes.

The results of this study make a strong case for expressing public-serving CSR motives and refraining from firm-serving CSR motives when communicating about a corporate-NGO partnership to consumers.

Focusing on the communication tactics of corporate-NGO partnerships extends existing literature by uncovering whether and how the factors driving effective communication in other CSR activities can be applied to the context of corporate-NGO partnerships.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.