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Objective: The objective of this study was to determine if and to what extent the Big Five personality factors of openness, conscientiousness, extraversion, agreeableness, or neuroticism individually moderate the relationship between the level of education and level of job satisfaction among contact center employees.   Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Herzberg’s Two Factor Theory and John’s BFI stand out, providing a solid basis for understanding the context of the investigation.   Method: The methodology adopted for this research comprises a quantitative correlational approach. Participants include U.S. contact center employees. Data collection was carried out through questionnaires distributed to contact center employees.   Results and Discussion: The results obtained revealed that the individual moderators of the BFI personality traits do not significantly impact the relationship between the level of education completed and the level of job satisfaction for U.S. contact center employees. In the discussion section, these results are contextualized in light of Herzberg’s Two Factor Theory, highlighting that education completion cannot be utilized as a motivator factor in this context even with the addition of the individual personality traits as moderators. Possible discrepancies and limitations of the study are also considered in this section.   Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of business and education. These implications could encompass human resources, management, and post-high-school education.   Originality/Value: This study contributes to the literature by offering a practical contribution to contact center professionals and educational institutions. The relevance and value of this research are evidenced by its potential to impact contact center hiring processes and assessing the value of post-high-school education for contact center employees.

Objective: The objective of this study is to investigate the relationship between the level of education completed after high school graduation and the level of job satisfaction among U.S. contact center employees.   Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Herzberg’s Two Factor Theory stands out, providing a solid basis for understanding the context of the investigation.   Method: The methodology adopted for this research comprises a quantitative correlational approach. Participants include U.S. contact center employees. Data collection was carried out through questionnaires distributed to contact center employees.   Results and Discussion: The results obtained revealed that the level of education completed after high school graduation does not significantly impact the level of job satisfaction for U.S. contact center employees. In the discussion section, these results are contextualized in light of Herzberg’s Two Factor Theory, highlighting that education completion cannot be utilized as a motivator factor in this context. Possible discrepancies and limitations of the study are also considered in this section.   Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of business and education. These implications could encompass human resources, management, and post-high-school education.   Originality/Value: This study contributes to the literature by offering a practical contribution to contact center professionals and educational institutions. The relevance and value of this research are evidenced by its potential to impact contact center hiring processes and assessing the value of post-high-school education for contact center employees.

Objective: The study seeks to determine the levels of work stress in the employees of a service company, describing the causes and their financial impact.   Theoretical Framework: The concepts of work stress defined as a general state of tension that triggers different emotional, cognitive, physiological and behavioral reactions are addressed, as well as some causing factors such as: Physical Environment, Job Demand, Knowing the tasks and interpersonal relationships.   Method: The study has a mixed approach, where qualitative categories were given quantitative management of the data, to find numerical evidence and be able to explain in more detail the presence of the event. The ILO-WHO Work Stress scale was used as an instrument, which was applied to 33 employees of a Contact Center company in the city of Manizales.   Results and Discussion: The results obtained revealed that work stress is below the limit of the intermediate stress range (81.09 / 90.02) and that due to Absenteeism and Turnover, the company has had costs of $43,995,586 COP according to approximate calculations.   Research Implications: The evidence from the study showed information that allows companies in the service sector to generate strategies for stress management and contribute to reducing costs due to absenteeism and turnover, the above could be used in other sectors or companies in the region.   Originality/Value: The value lies in contributing to the development of the state of the art on the subject and as a reference for future studies since the state of the art shows little research in Contact Center companies in Latin America, likewise for companies generating well-being strategies and the management of the stress.

PurposeThis paper aims to present a hybrid approach to measure the efficiency of virtual contact centers (VCCs) started during the pandemic and benchmark them for service performance. The results are used to plot the VCC's efficiency score (performance) and customer perception (Importance) to propose appropriate strategies.Design/methodology/approachUsing the survey method, 854 responses were collected from customers who used VCC services during the pandemic. This data was then employed to assess the performance of VCCs using SERVPERF and DEA methods, followed by the development of the model for performance analysis.FindingsResults reveal the ranking of different VCCs started during the pandemic for the telecom company using SERVPERF and DEA methods. Further, the performance analysis model highlighted the strategies appropriate for each VCCs.Practical implicationsThe findings add to the body of knowledge on how multiple service units of a large organization can assess service efficiency utilizing a combination of SERVPERF-DEA. The present work also contributes to the performance analysis field by proposing a model to assess the service centers and provide improvement guidelines.Originality/valueThe work is one of the first to assess the service efficiency of the VCCs started during the pandemic by using a unique hybrid approach of SERVPERF and DEA. This approach provides a direction to whom to benchmark and to what degree service quality should be improved. Further, the study proposes a unique performance analysis model based on performance scores and customer perception.

Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

The role of contact centers in improving the operational efficiency of numerous organizations is of utmost importance. Presently, digitalization technology has enabled contact centers to deliver exceptional customer service and support, while minimizing the adverse impact on agent well-being. Artificial intelligence techniques such as topic modeling and sentiment analysis can aid agents in addressing specific queries, providing real-time support and feedback, and helping them build stronger relationships with customers. This study aims to investigate the advantages of integrating these techniques in the analysis of customer–agent conversations within contact centers. This study examines whether there is a discernible advantage in analyzing customer–agent conversations in real-time and whether it is worth using this type of digitization to enhance agent performance and well-being. Furthermore, this study explores the impact of these technologies on European privacy, business, real-time agent support, the value of conversation data, brand reputation, and customer satisfaction. The results of this study demonstrate the significance of incorporating topic modeling and sentiment analysis into the analysis of customer–agent conversations at contact centers.

This paper develops methods to determine appropriate staffing levels in call centers and other many-server queueing systems with time-varying arrival rates. The goal is to achieve targeted time-stable performance, even in the presence of significant time variation in the arrival rates. The main contribution is a flexible simulation-based iterative-staffing algorithm (ISA) for the M t /G/s t + G model—with nonhomogeneous Poisson arrival process (the M t ) and customer abandonment (the + G ). For Markovian M t /M/s t + M special cases, the ISA is shown to converge. For that M t /M/s t + M model, simulation experiments show that the ISA yields time-stable delay probabilities across a wide range of target delay probabilities. With ISA, other performance measures—such as agent utilizations, abandonment probabilities, and average waiting times—are stable as well. The ISA staffing and performance agree closely with the modified-offered-load approximation, which was previously shown to be an effective staffing algorithm without customer abandonment. Although the ISA algorithm so far has only been extensively tested for M t /M/s t + M models, it can be applied much more generally—to M t /G/s t + G models and beyond.

Today, in many areas of technology, we can come across applications of various artificial intelligence methods. They usually involve models trained on some specific pool of learning data. Sometimes, however, the data analyzed by these solutions can change its nature over time. This usually results in a decrease in classification efficiency. In such a case, the use of techniques to retrain the originally trained reference models should be considered. One of the industries where the nature of data changes quite dynamically over time is the broadly defined call/contact center systems. An example of a module that is often found in this type of system and that, due to frequently changing marketing campaigns, requires the use of learning techniques is the automatic classification of text data. The paper describes the process of retraining the original reference models used in a multi-label text message classification method dedicated directly to call/contact center systems applications. In order to carry out the retraining process, Polish-language data from the actual archives of a large commercial contact center system and English-language data extracted from a publicly available database were used. The study was conducted for models based on artificial neural networks and bidirectional encoder representations from transformer type models. In addition, two different retraining strategies were studied, the results of which were compared with data obtained from the operation of reference models. As a result of the research work, an improvement of up to 5% in classification efficiency, as described by the metric Emotica was obtained, which means that proper integration of the retraining process brings tangible benefits to the solution tested in the article. Thus, it can also benefit the solutions used in business.

We consider large contact centers that handle two types of jobs—inbound and outbound—simultaneously, a process commonly referred to as call blending . Inbound work arrives to the system according to an exogenous arrival process, whereas outbound work is generated by the contact center. We assume that there is an infinite supply of outbound work to process, and that inbound calls are prioritized over the outbound calls. We propose a logarithmic safety staffing rule, combined with a threshold control policy, ensuring that agents' utilization is very close to one at all times, but that there are practically always idle agents present. Specifically, we prove that it is possible to have almost all inbound calls answered immediately upon their arrival, in addition to satisfying a target long-run throughput rate of outbound calls, with at most a negligible proportion of those calls dropped. Simulation experiments demonstrate the effectiveness and accuracy of our analysis. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Call centers usually handle several types of calls, but it is usually not possible or cost effective to have every agent be able to handle every type of call. Thus, the agents tend to have different skills, in different combinations. In such an environment, it is challenging to route calls effectively and determine the staff requirements. This paper addresses both of these routing and staffing problems by exploiting limited cross-training. Consistent with the literature on flexible manufacturing, we find that minimal flexibility can provide great benefits: Simulation experiments show that when (1) the service-time distribution does not depend on the call type or the agent and (2) each agent has only two skills, in appropriate combinations, the performance is almost as good as when each agent has all skills. We apply this flexibility property to develop an algorithm for both routing and staffing, aiming to minimize the total staff subject to per-class performance constraints. With appropriate flexibility, it suffices to use a suboptimal routing algorithm. Simulation experiments show that the overall procedure can be remarkably effective: The required staff with limited cross-training can be nearly the same as if all agents had all skills. Hence, the overall algorithm is nearly optimal for that scenario.