Diagnosing Service Success and Failure Incidents in the Consumer-to-Business Sharing Economy

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.

Diagnosing Service Success and Failure Incidents in the Consumer-to-Business Sharing Economy

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.

The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context

IR 4.0 is a new phase for the current trend of automation and data exchange in manufacturing industry that focuses on cloud computing, interconnectivity, the Internet of Things, machine learning, cyber physical learning and creating smart factory. The purpose of this article was to unveil the key factors of the IR 4.0 in Malaysian smart manufacturing context. Two key data collection methods were used: (1) primary data from the face-to-face interview (2) secondary data from the previous study. Significantly, five key factors of IR 4.0 consider for this study. Autonomous production lines, smart manufacturing practices, data challenge, process flexibility, and security. As a result, IR 4.0 for quality management practices might get high impact for the best performance assessment, which addressed in various ways; there are few studies in this area have been conducted in Malaysian manufacturing sector, and to recommend the best practices implemented from the managers’ perspectives. For scholars, this enhances their understanding and highlight opportunities for further research.

Automated Annotations for AI Data and Model Transparency

The data and Artificial Intelligence revolution has had a massive impact on enterprises, governments, and society alike. It is fueled by two key factors. First, data have become increasingly abundant and are often available openly. Enterprises have more data than they can process. Governments are spearheading open data initiatives by setting up data portals such as data.gov and releasing large amounts of data to the public. Second, AI engineering development is becoming increasingly democratized. Open source frameworks have enabled even an individual developer to engineer sophisticated AI systems. But with such ease of use comes the potential for irresponsible use of data. Ensuring that AI systems adhere to a set of ethical principles is one of the major problems of our age. We believe that data and model transparency has a key role to play in mitigating the deleterious effects of AI systems. In this article, we describe a framework to synthesize ideas from various domains such as data transparency, data quality, data governance among others to tackle this problem. Specifically, we advocate an approach based on automated annotations (of both data and the AI model), which has a number of appealing properties. The annotations could be used by enterprises to get visibility of potential issues, prepare data transparency reports, create and ensure policy compliance, and evaluate the readiness of data for diverse downstream AI applications. We propose a model architecture and enumerate its key components that could achieve these requirements. Finally, we describe a number of interesting challenges and opportunities.

Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience

Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science [ 84 ]. The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research pertaining to best practices for teaching in this complex domain that heavily relies on both computing and mathematical knowledge. We conducted a literature review and qualitative study with students and Higher Education lecturers from a range of educational institutions, with an aim to determine what might constitute best practices in this area in Higher Education. We hypothesised that confidence, mathematics anxiety, and differences in student educational background were key factors here. We then investigated the issues surrounding these and whether they inhibit the acquisition of knowledge and skills pertaining to the theoretical basis of artificial intelligence and machine learning. This article shares the insights from both students and lecturers with experience in the field of AI and machine learning education, with the aim to inform prospective pedagogies and studies within this domain and move toward a framework for best practice in teaching and learning of these topics.

Framework of Experimental Design and Data Mining in Multi-agent Simulation

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

Determinants of Muslim consumers’ halal cosmetics repurchase intention: an emerging market’s perspective

Purpose Due to a staggering growth rate in the recent past, halal products have attained a significant attention of marketers across countries. However, marketing practitioners seek to have detailed understanding of what drives consumers of different demographics towards this product category so as to better market and position themselves in the competitive landscape. Correspondingly, this study aims to provide insights into the Muslim women consumers’ halal cosmetics purchase behaviour and examines the variables (and their interplay) when purchasing such products. Design/methodology/approach The data for the study were collected through a self-administered questionnaire from 371 Muslim respondents from India. The data were analysed through structural equation modelling using AMOS 22.0 SEM software. Findings The findings of this study reveal that religious knowledge, religious commitment and halal certification(s) affect consumers’ actual purchase behaviour of halal cosmetics, which subsequently drives their repurchase intention. The findings further reveal a non-significant effect of religious orientation with both the actual purchase behaviour and repurchase intention towards halal cosmetics. Additionally, actual purchase behaviour of halal cosmetics is found to positively affect customers’ repurchase intentions. Originality/value Despite the recent growth of overall beauty industry, this particular segment of halal cosmetics has a huge potential given the phenomenal preference that Muslim consumers have shown in such niche. Therefore, this paper contributes towards examining the key factors influencing consumers purchase behaviour towards halal cosmetics in India that can be capitalized on.

Studying the Influence of Illumination Design in Quality Inspection on Vaccine Vials Production Line Using Computer Vision

In the industrial context, there are key factors that directly affect the system’s efficiency. Higher demands for both quantity and quality in today’s market call for constant research and development of technologies for automating production and quality control. Machine vision is a solution to increase speed and accuracy in defect detection. However, applications from machine vision are only effective if there is good data input. This is the reason why a machine vision system, needs high-quality input images from a well-designed illumination system. These illumination systems are designed to highlight faults in products. Therefore, the images obtained will provide optimized data for easier image processing thus directly increase the processing speed, accuracy, and overall system performance. To achieve this goal, this paper presents a few approaches to enhance and optimize images by implements illumination techniques into a miniature model of pharmaceutical bottle assembly line using machine vision as the inspector block. In this paper, we will evaluate the critical needs of using customize illumination system for quality inspection on an assembly line.