In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, ...design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed.
•Gamified work has a negative impact on FLEs’ job satisfaction and engagement.•FLE engagement is a driver of job performance.•Willingness to participate moderates gamified work effects on FLEs’ job ...satisfaction and engagement.
Rethinking the workplace experience as a means for enhancing the well-being of frontline employees (FLEs) represents a key priority for services. The well-being of frontline employees leads to improved performance and better customer service, such that it enhances the firm’s overall competitive advantage and revenue. Therefore, engagement-facilitating technologies that can increase FLEs’ well-being, such as gamified work, hold promise in terms of their effects on job satisfaction and engagement. Using a mixed-method design, including in-depth interviews with FLEs and their managers, and two large field experiments, this research considers two key sectors in which FLEs are critical: retailing and telemarketing. The results highlight the negative impacts of gamified work on employee engagement and well-being, although the willingness of employees to participate in such gamified work moderates these negative impacts. By revealing how gamification affects FLEs’ well-being, job engagement, and job satisfaction, this research provides actionable insights for managers.
Artificial intelligence (AI) applications in the market have become a buzzing trend. The current research proposed that consumers feel less empathy toward AI (vs. human) telesellers and thus tend to ...hang up on AI telesellers faster. Moreover, anthropomorphism (i.e., an individual tendency to attribute human qualities to nonhuman entities) moderates the above effect. Three studies provided evidence for the mediating role of empathy in the relationship between teleseller type and call duration and for the moderating role of anthropomorphism. We indeed found that the relationship between teleseller type and call duration via empathy is mitigated for consumers high in anthropomorphism.
•Consumers hang up on AI tele-sellers faster than their human counterparts.•Empathy toward tele-sellers serves as the underlying mechanism.•Consumers possessing higher anthropomorphism treat AI and human more similarly.
We propose a dynamic ensemble selection method, META-DES-AAP, to predict the success of bank telemarketing sales of time deposits. Unlike existing machine learning-based marketing sales prediction ...methods focusing only on prediction accuracy, META-DES-AAP considers the accuracy and average profit maximization. In META-DES-AAP, to consider both accuracy and average profit in the framework of dynamic ensemble selection using meta-training, a multi-objective optimization algorithm is designed to maximize the accuracy and average profit for base classifiers selection. Base classifiers suitable for each test telemarketing campaign are integrated according to the dynamic-based base classifiers integration method. Experimental results on bank telemarketing data show that META-DES-AAP achieves the best accuracy and the largest average profit when compared across several state-of-the-art machine learning methods. In addition, the factors influencing telemarketing on the average predicted probability of telemarketing success and average profit obtained by META-DES-AAP are analyzed.
Consumers have an increasingly wide variety of options available to entertain themselves. This poses a challenge for content aggregators who want to effectively promote their video content online ...through original trailers of movies, sitcoms, and video games. Marketers are now trying to produce much shorter video clips to promote their content on a variety of digital channels. This research is the first to propose an approach to produce such clips and to study their effectiveness, focusing on comedy movies as an application. Web-based facial-expression tracking is used to study viewers' real-time emotional responses when watching comedy movie trailers online. These data are used to predict both viewers' intentions to watch the movie and the movie's box office success. The authors then propose an optimization procedure for cutting scenes from trailers to produce clips and test it in an online experiment and in a field experiment. The results provide evidence that the production of short clips using the proposed methodology can be an effective tool to market movies and other online content.
Can artificial intelligence (AI) assist human employees in increasing employee creativity? Drawing on research on AI–human collaboration, job design, and employee creativity, we examine AI assistance ...in the form of a sequential division of labor within organizations: in a task, AI handles the initial portion, which is well-codified and repetitive, and employees focus on the subsequent portion, involving higher-level problem-solving. First, we provide causal evidence from a field experiment conducted at a telemarketing company. We find that AI assistance in generating sales leads, on average, increases employees' creativity in answering customers' questions during subsequent sales persuasion. Enhanced creativity leads to increased sales. However, this effect is much more pronounced for higher-skilled employees. Next, we conducted a qualitative study using semi-structured interviews with the employees. We found that AI assistance changes job design by intensifying employees' interactions with more serious customers. This change enables higher-skilled employees to generate innovative scripts and develop positive emotions at work, which are conducive to creativity. By contrast, with AI assistance, lower-skilled employees make limited improvements to scripts and experience negative emotions at work. We conclude that employees can achieve AI-augmented creativity, but this desirable outcome is skill-biased by favoring experts with greater job skills.
The development of information technology is expected that companies can create a variety of innovations so as not to decline. The research objective is to find out how much influence the quality of ...telemarketing communication on customer satisfaction X health insurance PT XYZ Insurance. The independent variable in this study is the quality of communication (X) with the concept of Joseph de Vito's theory (1997), and the dependent variable is customer satisfaction (Y) with the theory of Kotler, Keller (2008). This research uses a quantitative approach with a comparative causal type. The method used was a survey by distributing questionnaires. Sampling technique from a total population of 138 inpatients. The technique used in this study is total sampling where sampling is the same as the existing population. The results showed that the influence of communication quality on customer satisfaction health insurance X amounted to 0.27%, and the remaining 99.73% was influenced by other factors.
•Enhancing prediction performance for bank telemarketing by hybrid ensemble learning.•Providing more marketing decision information for banks by interpretability analysis.•Obtaining variables that ...significantly affect the prediction of bank telemarketing.•Analyzing how critical variables dynamically affect the success of bank telemarketing.
Because of the low cost and user-friendliness, telemarketing has become a common way for banks to obtain deposits for a long time. Meanwhile, researchers have been attempting to analyze consumer information in-depth to improve the effectiveness of bank telemarketing and attract deposits through telephone communication. In this paper, we construct bank telemarketing prediction models by three machine learning (ML) methods, i.e., Random Subspace (RS), Multi-Boosting (MB) and Random Subspace-Multi-Boosting (RS-MB), and find the best performing model. Also, we make the interpretability analysis to provide banks with decision information to develop and implement an effective marketing plan. We rank the importance of the original independent variables by the ML method and select those variables whose influence on the prediction results was significant. And we reconstruct the bank telemarketing prediction models based on the selected independent variables. Furthermore, we illustrate the marginal effects of the selected independent variables on the consumers’ subscription of deposits by the Partial Dependence Plots (PDP) to analyze how these selected independent variables affect the success of bank telemarketing campaigns. The empirical results indicate that the RS-MB using selected independent variables achieves the best performance for prediction. It is worth noting that banks would rather contact uninterested customers than miss potential deposit customers. Therefore, when predicting the success of telemarketing campaigns, it is more critical to reduce false negative rate than false positive rate. Moreover, banks using telemarketing should pay more attention to type of job that the customer does, the month that the customer was connected, and contact day of week.
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