Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis

The smartphone has made gaming more accessible and desirable for a wider market than ever before. Game apps are one of the most consumed and fastest growing products in the world today. Yet, few studies have thus far explored the implications of games apps consumption from a consumer perspective, addressing the transfer of abilities from one technological field to another. The main purpose of this paper is threefold: to ascertain the role of personality as a determinant of self-efficacy, to establish whether there is a transfer process from self-efficacy in video gaming with apps to online shopping and to analyze the impact of self-efficacy on the online purchase of game-related products. Results show that neuroticism, extraversion, and agreeableness determine the gaming self-efficacy that is transferred to online shopping self-efficacy and finally to the online purchase of game-related products. These insights provide interesting managerial implications that could affect video game marketing.

/pdf/the-path-between-personality-self-efficacy-and-shopping-57hu1gymgk.pdf

The Path between Personality, Self-Efficacy, and Shopping Regarding Games Apps

Occupational accidents are a public health problem; therefore, it is necessary to conduct research that contributes to accident prevention and health promotion. To this end, this study aimed to investigate the main factors influencing work accidents at an electric power company. A survey of 1,027 in-house and outsourced employees of a large electric utility was conducted. The participants included injured and non-injured professionals. Organizational, personal/behavioral, and work/task factors were found to have statistically significant effects on work accident occurrence. As an academic and managerial contribution, the identification of the main factors that influence work accidents in the electric sector favors the development of strategies and actions for its control and mitigation.

/pdf/factors-influencing-occupational-accidents-a-12e640t8e9.pdf

Factors influencing occupational accidents: a multidimensional analysis in the electricity sector

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers’ repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

The decisions made by the customers in online environments are influenced by their personality characteristics. Each customer in an online environment relies more heavily on certain features of a store to make decisions while ignoring others. Thus, personalizing these features may streamline the decision-making process and increase satisfaction. In this paper, an intelligent method for personalizing the features of an online store according to the users’ personality is presented. In the proposed method, using genetic programming several equations are developed to estimate how users with different personality characteristics prefer various features of an online store. These equations are then used for personalization of the store features to increase customers’ satisfaction and persuade them to make larger purchases. The evaluation on a sample of 194 individuals indicates that the obtained equations are able to estimate the user’s preferences with over 80% accuracy in most cases. In addition, empirical assessment of the obtained equations shows that the proposed personalization method improves the user satisfaction.

/pdf/personality-based-personalization-of-online-store-features-56zzwrpsos.pdf

Beenazir Ganji

Papers

Personality-Based Personalization of Online Store Features Using Genetic Programming: Analysis and Experiment