Investigating the view of quality management success factors amongst future early career operations leaders

Personal data protection compliance assessment: A privacy policy scoring approach and empirical evidence from Thailand's SMEs

Privacy policies, intended to provide information to individuals regarding how their personal data is processed, are often complex and challenging for users to understand. Businesses often demonstrate non-compliance with personal data protection laws, ranging from the absence of privacy policies to the existence of policies that do not adhere to legal requirements. This paper aims to (1) develop a quantitative and systematic tool for evaluating privacy policies' compliance with the Personal Data Protection Act (PDPA), (2) assess compliance among Small and Medium Enterprises (SMEs) in Thailand, and (3) provide recommendations for enhancing compliance practices. To achieve this, we proposed a multi-criteria privacy policy scoring model integrated with comprehensive statistical data analyses. The privacy policy scoring model consists of ten privacy principles and 31 privacy criteria, providing a structured framework for evaluating privacy policies. During a two-year postponement period for enforcing the PDPA law, we conducted a stratified random-sampling survey of 384 SMEs to evaluate their privacy policies using the proposed scoring model. The accomplished results revealed significantly lower scores than anticipated, with the nationwide average score of SMEs reaching only 6.1909 out of 100 points. More than half of the SMEs collected personal data without announcing privacy policies, and those with privacy policies adhered to an average of only 12.15 out of 31 privacy criteria. These findings highlight the pressing need to improve compliance practices among SMEs in Thailand. The proposed methodology can be customized and applied to align with the requirements of personal data protection laws in other countries. Additionally, our findings indicate that compliance with the PDPA is influenced by the Thailand Standard Industrial Classification (TSIC) sections, suggesting the adoption of tailored approaches by policymakers to address the specific needs of different TSIC sections.

Interval valued Pythagorean fuzzy aggregation operators based Malcolm Baldrige National Quality Award assessment

Malcolm Baldrige National Quality Award (MBNQA) is a quality assessment and rewarding system that aims to increase the awareness of quality management. Although the award is launched in the USA in 1989 and only given to the U.S based companies, it is recognized internationally. There are 7 types of categories in the award system (Leadership, Strategic planning, Customer focus, Measurement, analysis, and knowledge management, Workforce focus, Process management, and Results) where the evaluation is made over 1000 points and each category has its own weight. Since almost all the publications in the literature are based on crisp measurements and evaluations of the system performances, we proposed a multi attribute decision making (MADM) method using interval valued Pythagorean fuzzy weighted averaging (IVPFWA) and interval valued Pythagorean fuzzy weighted geometric (IVPFWG) aggregation operators for MBNQA assessment to represent the decision makers’ subjective evaluations better. A comparison of the results with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and an illustrative example are presented in the study.