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Problem definition: This paper considers a tandem queueing system in which stage 1 has one station serving multiple classes of arriving customers with different service requirements and related delay costs, and stage 2 has multiple parallel stations, with each station providing one type of service. Each station has many statistically identical servers. The objective is to design a joint capacity allocation between stages/stations and scheduling rule of different classes of customers to minimize the system's long-run average cost. Methodology/results: Using fluid approximation, we convert the stochastic problem into a fluid optimization problem and develop a solution procedure. Based on the solution to the fluid optimization problem, we propose a simple and easy-to-implement capacity allocation and scheduling policy and establish its asymptotic optimality for the stochastic system. The policy has an explicit index-based scheduling rule that is independent of the arrival rates, and resource allocation is determined by the priority orders established between the classes and stations. We conduct numerical experiments to validate the accuracy of the fluid approximation and demonstrate the effectiveness of our proposed policy. Managerial implications: Tandem queueing systems are ubiquitous. Our results provide useful guidelines for the allocation of limited resources and the scheduling of customer service in those systems. Our proposed policy can improve the system's operational efficiency and customers' service quality. Funding: Z. Zhong's research is partially supported by the Fundamental Research Funds for the Central Universities [Grant 2023ZYGXZR074] and the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40109]. P. Cao's research is partially supported by the National Natural Science Foundation of China [Grant 72122019]. J. Huang's research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14501621] and the National Natural Science Foundation of China [Grant 72222023]. S. X. Zhou's research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14500921], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0266. [ABSTRACT FROM AUTHOR]

Problem definition: We study customer service chat (CSC) systems, in which agents can serve multiple customers simultaneously, with generally distributed service and patience times. The multitasking capability of agents introduces idiosyncratic challenges when making routing and staffing decisions. Methodology/results: To determine the dynamic matching of arriving customers with available agents, we first formulate a routing linear program (LP) based on system primitives. Inspired by the optimal solution of the routing LP, we design a parsimonious dynamic routing policy that is independent of arrival rate and service capacity information. We also use the optimal solution to develop closed-form approximations for crucial performance metrics and show that a similar LP can be utilized to make staffing decisions. Through extensive simulation experiments, we showcase the efficacy of our approximations and staffing decisions. Furthermore, under our proposed policy, the CSC system exhibits a unique stationary fluid model in which the steady-state performance measures align with our approximations. Managerial implications: The extant literature primarily focuses on Markovian systems with exponential distributions. In this paper, customers' service and patience times are allowed to be generally distributed to agree with practical settings. Our findings indicate that the distributions have a significant impact on routing policies, staffing decisions, and system performance. Funding: Z. Long was supported by the National Natural Science Foundation of China [Grants 72101112, 72132005, and 72271119] and Jiangsu Province, China [Grant BK20210171]. J. Zhang was supported by the Hong Kong Research Grants Council, General Research Fund [Grants 16208120 and 16214121]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0114. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to shape, design and present a model that can be used to develop the level of customer service through social networks in the banking system. For this purpose, a large part of the mentioned structures was extracted using the ultracomposite method. Therefore, in the first step, an extensive search was conducted among various sources (including books, book summaries, periodicals, case studies, doctoral dissertations, etc. based on keywords) and more than four hundred sources were collected. Then, by separating the categories, themes and coding, using qualitative data analysis software, the results will be extracted and finally, multiple structures will be extracted to compile the model. In the following, by using ”content validity indices and content validity ratio”, the validity of the desired measures was measured by the opinions of the experts and then with the limited use of questionnaire items, the reliability of the items that make up the functional structures, as well as the reliability of the questionnaire tool (alpha Cronbach’s) calculation and the result was 0.89. [ABSTRACT FROM AUTHOR]

In this study, we investigate the container delivery problem and explore ways to optimize the complex and nuanced system of inland container shipping. Our aim is to fulfill customer demand while maximizing customer service and minimizing logistics costs. To address the challenges posed by an unpredictable and rapidly-evolving environment, we examine the potential of leveraging reinforcement learning (RL) to automate the decision-making process and craft agile, efficient delivery schedules. Through a rigorous and comprehensive numerical study, we evaluate the efficacy of this approach by comparing the performance of several high-performance heuristic policies with that of agents trained using reinforcement learning, under various problem settings. Our results demonstrate that a reinforcement learning approach is robust and particularly useful for decision makers who must match logistics demand with capacity dynamically and have multiple objectives. [ABSTRACT FROM AUTHOR]

Customer relationship management is complicated by a lack of understanding of customer attributes and needs. Transformational and inventive techniques are needed to improve customer service. WhatsApp-based AI Chatbots can improve academic service efficiency and quality. The platform's usability and interaction with customer management systems are the main reasons. By adopting the AI Chatbot, clients and internal users will receive consistent and correct responses. Qualitative interviews and survey analysis are used to collect data for the study. Qualitative interviews are utilized for qualitative analysis, while survey analysis validates research survey data. The report reveals 83.8% customer satisfaction and 75.3% customer loyalty. The study found that the chatbot's ability to minimize service time greatly affected customer satisfaction. Due to their consistency and speed, chatbots in customer care have improved consumer satisfaction. Customers like chatbots' speed and efficiency, which allow them to get help immediately. The chatbot's ability to handle common questions and tasks frees up staff to handle more complex and urgent issues, improving service. [ABSTRACT FROM AUTHOR]

Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users' demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints. [ABSTRACT FROM AUTHOR]

The article focuses on the necessity for courts to adopt modern, technology-driven customer service features. Topics include the use of remote court appearances via telephone or video, the integration of smartphone evidence presentation in courtrooms, and the implementation of responsive, mobile-friendly court websites.

Copyright of Opuntia Brava is the property of Universidad de Ciencias Pedagogicas de Las Tunas, Centro de Documentacion e Informacion Pedagogica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tourism & Rural Development Studies / Turisztikai és Vidékfejlesztési Tanulmányok is the property of CampInvest Kft. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Natural answer generation is in a very clear practical significance and strong application background, which can be widely used in the field of knowledge services such as community question answering and intelligent customer service. Traditional knowledge question answering is to provide precise answer entities and neglect the defects; namely, users hope to receive a complete natural answer. In this research, we propose a novel attention-based recurrent neural network for natural answer generation, which is enhanced with multi-level copying mechanisms and question-aware loss. To generate natural answers that conform to grammar, we leverage multi-level copying mechanisms and the prediction mechanism which can copy semantic units and predict common words. Moreover, considering the problem that the generated natural answer does not match the user question, question-aware loss is introduced to make the generated target answer sequences correspond to the question. Experiments on three response generation tasks show our model to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 0.727 BLEU on the SimpleQuestions response generation task, improving over the existing best results by over 0.007 BLEU. Our model has scored a significant enhancement on naturalness with up to 0.05 more than best performing baseline. The simulation results show that our method can generate grammatical and contextual natural answers according to user needs. [ABSTRACT FROM AUTHOR]

Enhancing the naturalness and rhythmicity of generated audio in end-to-end speech synthesis is crucial. The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on text and spectrum data from the training set. Particularly, the posterior encoder struggles with mid- and high-frequency feature extraction, impacting waveform reconstruction. Existing efforts mainly focus on prior encoder enhancements or alignment algorithms, neglecting improvements to spectrum feature extraction. In response, we propose BERTIVITS, a novel model integrating BERT into VITS. Our model features a redesigned posterior encoder with residual connections and utilizes pre-trained models to enhance spectrum feature extraction. Compared to VITS, BERTIVITS shows significant subjective MOS score improvements (0.16 in English, 0.36 in Chinese) and objective Mel-Cepstral coefficient reductions (0.52 in English, 0.49 in Chinese). BERTIVITS is tailored for single-speaker scenarios, improving speech synthesis technology for applications like post-class tutoring or telephone customer service. [ABSTRACT FROM AUTHOR]

The modern language generation model ChatGPT, created by Open Artificial Intelligence (AI), is recognised for its capacity to comprehend context and produce pertinent content. This model is built on the transformer architecture, which enables it to process massive volumes of data and produce text that is both cohesive and illuminating. Service is a crucial component everywhere as it provides the basis for establishing client rapport and offering aid and support. In healthcare, the application of ChatGPT for patient service support has been one of the most significant advances in recent years. ChatGPT can help overcome language obstacles and improve patient satisfaction by facilitating communication with healthcare personnel and understanding of care. It can assist in enhancing the entire patient experience by offering personalised information and support to patients and making it more straightforward for them to communicate with healthcare professionals. Its goal can be to expedite and streamline service by promptly and accurately responding to customers. Businesses of all sizes increasingly use ChatGPT since it allows them to provide 24/7 customer support without requiring human contact. This paper briefly discusses ChatGPT and the need for better services. Various perspectives on improving customer and patient services through ChatGPT are discussed. The article also discussed the major key enablers of ChatGPT for refining customer and patient assistance. Further, the paper identifies and discusses the critical application areas of ChatGPT for customer and patient service. With its ability to handle several requests simultaneously, respond quickly and accurately to client questions, and gain knowledge from every interaction, ChatGPT is revolutionising customer and patient service. Its accessibility and compatibility with various communication channels make it a desirable solution for businesses looking to improve support. As technology advances, ChatGPT is positioned to become an essential tool for businesses wishing to provide speedy and customised service. Although ChatGPT may give convincing solutions, the chance of providing accurate and updated information poses a problem for its usage in service jobs that need accurate and up-to-date information. In future, various services will become better and more efficient due to ChatGPT and AI. [ABSTRACT FROM AUTHOR]

The article explores the impact of artificial intelligence-based chatbot technologies on enhancing communication and sales in the insurance sector. Topics discussed include the benefits of chatbots for customer service and sales support, challenges related to data privacy and user trust, and the role of anthropomorphism in improving customer engagement and satisfaction.

With the proliferation of the use of chatbots across industries, business-to-business (B2B) businesses have started using cognitive chatbots for improved customer service which signifies our research. By extending the Technology Acceptance Model and Information Systems Success Model, this study examines personalised contextual customer service using cognitive chatbot. A quantitative research method is applied to the primary data collected from 300 respondents of B2B businesses. The study contributes to the limited research on chatbots and suggests improvement in customer service. The findings provide evidence of high value by customers, particularly while checking for real-time information on reliability and accessibility of products/services. The automated answers to repetitive questions on the recurrent issues create a seamless experience for the customers. This research makes significant theoretical contributions by integrating two models into a simplified model in chatbot literature and manifest that trust affects the willingness to use the cognitive chatbot which drives automation. [ABSTRACT FROM AUTHOR]

This study explored the criteria for evaluating human–chatbot collaboration in customer service using the socio-technical systems theory. Interviews with 28 customer service managers, conversational designers, and human agents revealed their evaluation criteria diverged. Managers prioritized the chatbot's technical capabilities and preferred a standalone customer service chatbot, while conversational designers prioritized the social aspect, focusing on the chatbot as a support tool for human agents and customers. Human agents mainly evaluated the chatbot as a means to increase job satisfaction. These distinct criteria highlight the importance of an aligned approach in human–chatbot collaboration. To improve this collaboration, integrating the chatbot into customer data systems was suggested. [ABSTRACT FROM AUTHOR]

Given the challenges that businesses have when it comes to adjusting to current circumstances and improving their door-to-door delivery processes, it has been observed that delivery agents are the primary cause of troubles. Ensuring reliable transportation of air-to-sea cargo on a global level continues to provide a significant and difficult obstacle. The absence of a formal contractual arrangement between worldwide air and sea freight intermediaries and their clients has resulted in the prompt settlement of various concerns, such as missing merchandise, shipment delays, and damaged goods. The current study used a quantitative research approach to examine three hypotheses by considering four variables: "Assurance," "Legal Risk," and "Informal Contract" as the independent variables, and "Delivery of package by agents" as the dependent variable. The results of the study demonstrate that all of the independent variables exerted a significant influence on the dependent variable. The study revealed that "assurance" and "perceived legal risk," exert a beneficial influence on the establishment of informal contracts between Malaysian international air and sea cargo-agents and their customers in the context of shipping. The variable of "assurance" has a notable influence on the "Delivery of package by agents". The findings also carry substantial significance for customers, as it offer valuable insights into the uncertainties and obstacles associated with the informal agreements between Malaysian international air and sea cargo agents and their customers pertaining to shipping. [ABSTRACT FROM AUTHOR]

Telecommunications operators are tasked with enhancing service quality, reducing operational costs, and preserving customer privacy. This study presents an innovative application of large language models (LLMs) integrated with the LangChain technology framework, aimed at revolutionizing customer service in the telecom sector. The LangChain framework features a Knowledge Organizing Module and a Knowledge Search Module, both designed to refine customer support operations. The research develops an LLM‐based approach to improve the segmentation and organization of knowledge bases, tailored for the telecommunications industry. This approach ensures seamless integration with existing LLMs while preserving distinct knowledge domains, crucial for search accuracy. Additionally, the framework includes an advanced information security protocol with a robust filtering system that effectively excludes sensitive data from the model's outputs, enhancing data security. Empirical findings indicate that the ChatGLM2‐6B+LangChain model outperforms the baseline ChatGLM2, demonstrating heightened effectiveness in telecom‐specific tasks and outstripping even more sophisticated models like GPT‐4. The implementation of this LLM‐based framework within telecom customer service systems has significantly sharpened the precision of knowledge recommendations, as reflected by a dramatic increase in acceptance rates from 15% to 70%. [ABSTRACT FROM AUTHOR]

Service organizations, particularly in the hospitality sector, are constantly under pressure to meet the ever-increasing and ever-changing expectations of the customers, in an innovative manner; making employee creativity a mainstream demand. Researchers and practitioners are pondering ways to frame employee creativity in the hospitality industry. This study aims to empirically investigate the role of positive affect in framing employee creativity in the hospitality industry. The broaden-andbuild theory was employed to explain the framework under study, examining relations between affective, cognitive, psychological, and social factors that may shape employee creativity in the hospitality industry. Data was collected from 391 employees, working in hotels and restaurants in Pakistan, on a structured survey, by using a purposive sampling approach. The PLSSEM approach was considered for data analysis. The findings show a positive relationship between positive affect and perspective-taking. The previously understudied relationship between perspective-taking and thriving at work was found significant. The relationship of perspective-taking with structural and relational social capital was also found significant. Supporting prior research, our results show that thriving at work and relational social capital lead to employee creativity. Interestingly, the relationship between structural social capital and employee creativity was insignificant. This study provides valuable insights for practitioners and hospitality managers to shape employee creativity by considering positive affect as a stimulator. [ABSTRACT FROM AUTHOR]

The influential roles of supervisor's leadership in the context of a small store are not studied and it is unclear how it affects the attitudinal and behavioral outcomes of frontline service employees. Drawing on the social exchange theory, the purpose of this study is three folds. First, this study aims to examine the role of supervisors' authentic leadership and supervisor ostracism on frontline service employees' perceived supervisor trust. Second, this study investigates the effects of frontline service employees' perceived supervisor trust on commitment to customer services and store affective commitment. Finally, the positive role of store affective commitment on frontline service employees' commitment to customer services is examined. A survey-based was used to collect 203 responses from frontline service employees of coffee shops in Korea. Covariance-based structural equation modeling (CB-SEM) was conducted to test the hypotheses. The results indicated that a supervisor's authentic leadership is positively whereas supervisor ostracism is negatively associated with supervisor trust. Similarly, supervisor trust has a significant positive effect on the frontline service employee's commitment to customer services and store affective commitment. Finally, frontline service employees' store affective commitment has a significant positive effect on the commitment to customer services. This research offers practical implications for managers of frontline service employees, particularly in the selection, training, promotion, and socialization process of supervisors of small stores in general and in coffee shops in particular. Practitioners interested to increase frontline service employees' commitment to customer services and store affective commitment can leverage the findings of this study. [ABSTRACT FROM AUTHOR]

Background: Career stress is one of the most important and challenging risk factors among employees because of its vast effect on mental and physical health. This research was conducted with the aim to compare the effectiveness of mindfulness-based stress reduction (MBSR) and bioenergy economy-based health improvement (BEHI) program on occupational stress and psychosomatic symptoms of distressed employees. Methods: This quasi-experimental study was conducted using a pretest-posttest design. The targeted population of the study comprised all employees of the customer service management department of Telecommunications Company in Isfahan, Iran, in the year 2018. Purposeful sampling was employed, and 60 candidates exhibiting distress symptoms were randomly assigned to 3 groups (MBSR, BEHI, and control groups), each consisting of 20 individuals. Data collection was performed using the Kessler Psychological Distress Scale (K6; Kessler et al., 2003), Stressful Life Event (SLE) Questionnaire (Roohafza et al., 2011), Occupational Stress Questionnaire (Cousins et al., 2004), and Screening for Somatoform Symptoms-7 (SOMS-7) questionnaire (Ebrahimi et al., 2018). The experimental groups participated in the MBSR (Kabat-Zinn, 1990) and BEHI (Goli, 2022) program training sessions. The control group received no intervention. The data were assessed in SPSS software. Results: Both the BEHI program and MBSR therapy significantly decreased the occupational stress (F = 2.78) and psychosomatic symptoms (F = 3.63) of distressed employees (P < 0.05). However, there was no significant difference between the mean scores of the experimental groups in the posttest (P > 0.05). Conclusion: Both programs could reduce the employees' distress on worksites and could be beneficial for healers to mitigate the psychosomatic manifestations of occupational stressors. [ABSTRACT FROM AUTHOR]

Copyright of Retos, Revista de Ciencias Administrativas y Económicas is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to develop an empathetic and user-centric customer support service design model. Though service design has been a critical research focus for several decades, few studies focus on customer support services. As customer support gains importance as a source of competitive advantage in the present era, this paper aims to contribute to industry and academia by exploring the service design model. Design/methodology/approach: The study adopted a theories-in-use approach to elucidate mental models based on the industry's best practices. In-depth interviews with 62 professionals led to critical insights into customer service design development, supported by service-dominant logic and theory of mind principles. Findings: The ensuing insights led to a model that connects the antecedents and outcomes of empathetic and user-centric customer service design. The precursors include people, processes and technology, while the results are user experience, service trust and service advocacy. The model also emphasises the significance of the user's journey and the user service review in the overall service design. Research limitations/implications: The model developed through this study addresses the critical gap concerning the lack of service design research in customer support services. The key insights from this study contribute to the ongoing research endeavours towards transitioning customer support services from an operational unit to a strategic value-creating function. Future scholars may investigate the applicability of the empathetic user service design across cultures and industries. The new model must be customised using real-time data and analytics across user journey stages. Practical implications: The empathetic and user-centric design can elevate the customer service function as a significant contributor to the overall customer experience, loyalty and positive word of mouth. Practitioners can adopt the new model to provide superior customer service experiences. This original research was developed through crucial insights from interviews with senior industry professionals. Originality/value: This research is the original work developed through the key insights from the interview with senior industry professionals. [ABSTRACT FROM AUTHOR]

Artificial intelligence plays an important role in the field of personal computers. Right now personal computers are part of our lives, so AI should be used in all everyday tasks. Humans are great thinkers, but machines can be more effective at counting than humans. The machine cannot fully explain different conditions, but it can create a different type of connection between different salient points and features. Either way, there can be many benefits to establishing machine learning computing in our daily lives. Machine learning or machine learning is one of the subsets of artificial intelligence that enables systems to learn and improve automatically without explicit programming, and controlling the credit risk of real bank customers is one of these benefits that can help the monetary and banking system to improve conditions and reduce risk. Therefore, the use of machine learning to create an algorithm to manage credit risks is a topic addressed in this research. [ABSTRACT FROM AUTHOR]

Luxury brands in various industries are one of the most attractive and profitable segments of the market, which, in addition to economic advantage, helps to develop the quality level of those products in the world by creating competition among luxury brands. Meanwhile, marketers and brand owners are constantly creating loyalty programs for their fanatical consumers. The purpose of this research is to identify the mental pattern of fanatical consumers towards luxury brands. The current research is of mixed exploratory type and was done using the Q method. The statistical population of this research is the customers of luxury watch brands. A sufficient number of statistical samples were asked based on theoretical saturation in a purposeful and accessible manner, six experts familiar with the subject were interviewed, and in a small part, 38 fanatical customers were interviewed to sort sample expressions into They were selected purposefully. The mental model of obsessive consumers included four types: devoted, deceitful, pleasure-seeking, and dysfunctional, which explained about 79% of the variance of the views. Fanatic and dedicated consumers were the dominant model of this research. Finally, implementation suggestions were presented for each of the mental patterns of fanatical consumers for the use of luxury brand owners. [ABSTRACT FROM AUTHOR]

Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user's dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the "hallucination" phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods. [ABSTRACT FROM AUTHOR]

Abstract Although the move to more service‐oriented business can be beneficial even to smaller firms, servitization in SMEs remains a largely unexplored topic. The authors contribute to fill this gap exploring how SMEs can overcome the knowledge gaps of servitization faced by companies in the early‐stages of this journey. By combining systematic literature review and expert panel methodology, the authors identify three knowledge gaps that hinder servitization initiatives in SMEs and propose a set of managerial recommendations to tackle with these gaps. In particular, the authors suggest a structured plan of recommendations, and point out how each stakeholder can contribute to fill the mentioned gaps. The proposed actions are specifically suggested for SMEs and focus on greater engagement of internal and external stakeholders. In addition to contributing to the domain scientific research on servitization, the authors therefore respond to the call for application‐oriented research.

Two strategic factors of any business are customer interaction (how close you are to your customers) and customer participation (how involved customers are in producing the offering). In recent years, we have seen companies increase interaction through servitization and increase customer participation through self-service technologies. Yet, more is not necessarily better. Too much customer interaction can destroy operating efficiencies. Too much customer participation can compromise quality and depersonalize service relationships. This article provides a framework for analyzing customer interaction and participation, including an outline of decision factors, with the goal of identifying optimal and sustainable positioning for any given offering. [ABSTRACT FROM AUTHOR]

With the popularity of online reviews, brand managers have opportunities to segment their markets according to the reviews of their products or services by customers. Nonetheless, it has been suggested that traditional market segmentation methods are ineffective at analyzing online review data due to the complex features and large amount of this type of data; specifically, traditional methods fail to take into account the networked nature of interactive relationships among reviewers and brands across online review websites. Accordingly, this study proposes a network analysis approach for the market segmentation of online reviews. Collecting samples from Yelp via web scraping, this study demonstrates how network analysis techniques can be utilized to segment online reviewers through a four-step process. The results reveal the core and peripheral market segments, as well as the bridge segment in the core. The study contributes to offering marketing researchers and managers a new network structure analysis approach for the market segmentation of online reviews. [ABSTRACT FROM AUTHOR]

Robots have tremendous potential, and have recently been introduced not only for simple operations in factories, but also in workplaces where customer service communication is required. However, communication robots have not always been accepted. This study proposes a three-stage (first contact, interaction, and decision) model for robot acceptance based on the human cognitive process flow to design preferred robots and clarifies the elements of the robot and the processes that affect robot acceptance decision-making. Unlike previous robot acceptance models, the current model focuses on a sequential account of how people decide to accept, considering the interaction (or carry-over) effect between impressions established at each stage. According to the model, this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed during the interaction with robot (politeness of its conversation and behavior) on robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior, the greater the acceptance rate. Importantly, there was no interaction between these two factors. The results indicating that the impressions of the first contact and interaction are additively processed suggest that we should accumulate findings that improving the appearance of the robot and making its communication behavior more human-like in politeness will lead to a more acceptable robot design. [ABSTRACT FROM AUTHOR]

In this paper, we adopt an exploratory, descriptive single-case study design in a food wholesale industry to illustrate (i) how the personal AI assistant adds value to the sales process and the company, and (ii) how human-like top expert AI suggestions enhance sales people competences. The significance of the study stems from its positioning in the identified research gap by presenting the scalable best product-market fit AI solution for hyper-personalized customer care in the sales process. In addition, the findings of a case study are discussed in comparison with previous related research, providing with technological and organizational AI characteristics systematization. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we examine the transformation of big data in the banking industry, where data has become a major asset. The advent of big data analytics is bringing about a major shift in how banks operate, interact with customers and make strategic decisions. The article first examines big data, focusing on its key features: volume, velocity, and diversity, then its impact on banking operations and then provides a comprehensive analysis of the origins of big data in its banking sector of the industry, including marketing history, customers and communications, social media engagement, and others. In addition to contributing to the banking industry's vast database, the paper outlines the many ways banks are using big data, from improving risk management to improving customer services to fraud by identifying activities and increasing operational efficiency. Finally, the article addresses big data challenges for banking, such as data privacy and security issues, and provides case studies to illustrate the tangible benefits that banks gain by embracing big data analytics acknowledge its value. [ABSTRACT FROM AUTHOR]

The research aims to provide the decision-maker with a framework for determining customer requirements during product development. The proposed framework is based on sentiment analysis and supervised multilabel classification techniques. Therefore, the proposed technique can categorise customer reviews based on the "product design criteria" label and the "sentiment of the review" label. To achieve the research goal, the research presented in this article uses the existing product development framework presented in the literature. The modification is conducted especially in the conceptual stage of product development, in which the voice of the customer or a customer review is obtained from the scraping, and a multilabel classification technique is performed to categorise customer reviews. The proposed framework is tested by using the set data on women's clothing reviews from an e-commerce site downloaded from www.kaggle.com based on data by Agarap (2018). The result shows that the proposed framework can categorise customer reviews. The research presented in this paper has contributed by proposing a technique based on sentiment analysis and multilabel classification that can be used to categorise customers during product development. The research presented in this paper answers one of the concerns in the categorisation of needs raised by Shabestari et al. (2019), namely, the unclear rules or main attributes of a requirement that make these needs fall into certain categories. Categorising customer requirements allows decision-makers to determine the direction of product development to meet customer needs. [ABSTRACT FROM AUTHOR]

The User Experience Questionnaire (UEQ) is a commonly used tool for measuring product experience. This study covers extending the UEQ to measure multimodal experiences that include both product and service experiences. Currently, no questionnaires measure holistic user experiences, including pragmatic and hedonic qualities, for both product and service experiences. Through three study phases, we created and tested the Generalized User Experience Questionnaire (UEQ-G). First, we generalized and tested language from the UEQ's original, product experience context. Second, the UEQ-G was applied to controlled service experiences in which conditions were artificially manipulated across traditional UEQ factors. Third, we applied the UEQ-G in the field to experiences that contained both product and service experiences within the same scenario. No significant differences were observed between the UEQ and UEQ-G during the first phase, but the UEQ-G detected differences between high and low conditions for all expected factors except one during the second phase. During the third phase, many expected correlations were found among UEQG factors and those from other well-established tools; however, a few expected correlations were not observed. This study found the UEQ-G to be as valid and reliable as its predecessor, UEQ, in product experience scenarios, and although additional study is required, the UEQ-G showed great potential in evaluating service experience scenarios and for evaluating multimodal experiences in the field. With additional study, the UEQ-G tool could be the first tool of its type for assessing holistic user experience across various multimodal experiences. [ABSTRACT FROM AUTHOR]

The purpose of this article is to explore the concept of ikram al-dayf and its potential to be adapted into the Muslim-friendly tourism and hospitality practices. This article also introduces a set of ethical values for honoring guests in reference to the concept of ikram al-dayf. The concept of ikram al-dayf is pertinent to improve the Muslim-friendly staffs’ knowledge and understanding on how they should treat their guests better, since their service quality especially related to customer service aspect is still questionable. The article in the beginning presents the Muslim-friendly tourism and hospitality industry scenario and its related standards to show the needs for quality Muslim-friendly guest service. Next, it describes the established dimensions of service quality (SERVQUAL) model. Third, it explains the concept of ikram al-dayf and its relationship to guest service quality. It is based primarily on evidence from the Qur’an and the hadith. Fourth, it inserts ethical values based on the concept of ikram al-dayf into the dimensions of SERVQUAL. Finally, it briefly outlines the importance of applying ikram al-dayf ethics to be applied in Muslim-friendly hospitality practices. The article highlighted four core ethical values, namely qawlan karima, amanah, ta’ awun and ihsan which need to be given special attention for the best quality of service. These four ethical values suit the four dimensions of service quality and can be applied in the guest service phases (prearrival, arrival, stay, and departure of guests). The findings can then be embedded in the existing SERVQUAL dimensions as an added value for the use of Muslim-friendly hospitality players. The appreciation of ikram al-dayf ethics is not merely meant to portray the beauty of Islamic morality but also to improve service quality and achieve higher customer satisfaction. Eventually, it will be a competitive advantage for Muslimfriendly hospitality services to attract more customers especially among Muslims guests. [ABSTRACT FROM AUTHOR]

The present study discusses the impact of Human-Robot Collaboration (HRC) powered by Artificial Intelligence (AI) on customer service. It is based on the four types of intelligence – mechanical, analytical, intuitive, and empathetic – and how they are integrated into HRC to provide customers with more efficient and personalized services. The benefits of AI-enabled HRC are highlighted, including reduced operational costs, increased productivity, improved decision-making, and enhanced customer experience. However, the article also addresses the challenges of implementing this approach, such as the potential loss of jobs due to automation, and emphasizes the importance of ethical and responsible implementation. The study has significant practical and academic implications, warning that continuous research is needed to understand the potential and limitations of AI-enabled HRC on customer service. Overall, through a literature review, the article aims to appeal to the reader's critical spirit and explore topics on the transformative power of AI in customer service. [ABSTRACT FROM AUTHOR]

With the advent of AI, consumer-brand interactions are undergoing a dramatic transformation (Prikshat, Malik, and Budhwar, 2021). Consumer experience with AI is a lesser explored area of study. This research aims to examine how AI may be used in the retail industry to enhance the purchasing experience for AI users. With the trust-commitment theory and the service quality model as our inspiration, we developed our theoretical framework. Customers who have used a YouTube service powered by artificial intelligence were sent an online survey. Partial least squares-structural equation modeling was used to analyze the data (n=301 replies). Trust and relationship commitment was shown to play a pivotal role in moderating the connections between customers' evaluations of service convenience, individualization, and quality. We also discovered that trust is not the only factor that substantially influences relationship commitment and that relationship commitment acts as a mediator between service quality and AI-enabled customer experiences. This research fills a gap in the literature by identifying the direct influence of perceived convenience, personalization, and service quality on AI-enabled customer experience and the intermediary effects of trust and relationship commitment. Further, the research has real-world consequences for businesses that want to apply AI in the services they provide to YouTube subscribers. [ABSTRACT FROM AUTHOR]

The objective of the research is to present future scenarios for the business fabric of the municipalities of Sevilla and Caicedonia, located in the Valle del Cauca, Colombia. These scenarios have been developed using the Prospective SWOT approach, Régnier's Abacus, and the Impact Intensity Assessment Matrix. Their purpose is to provide valuable input for collective reflection and decisionmaking aimed at building a desired future. The analysis reveals that the business fabric of the municipalities of Sevilla and Caicedonia faces key challenges in resources, quality of proposals, customer service, technology, and innovation. Overcoming these difficulties is crucial to seize opportunities, as the perceptions of the stakeholders indicate a willingness to address the challenges. Despite having strengths, these are not sufficient, and it is vital to reduce weaknesses to face threats. Expanding market knowledge and paying attention to new opportunities are essential, and future scenarios have been used as reflective tools to inspire a shared vision and establish strong foundations in the face of strategic challenges in these municipalities. [ABSTRACT FROM AUTHOR]

The article deals with the methodical foundations of quality management of railway transport services. Currently, the transport industry of Ukraine is going through extremely difficult times. This is due to the strengthening of internal conflicts regarding the development of enterprises in the industry, as well as negative external factors related to economic and political activities and a national disaster. Quality management at railway transport enterprises involves the creation of quality management systems and measures necessary for their effective functioning. It is noted that the profit dynamics of railway transport enterprises largely depend on socio-economic factors regarding the needs and possibilities of using high-quality transport services, as well as on technical and technological possibilities. As a result, the main disagreements and problems that led to customer dissatisfaction with rail transport services were revealed. As a result, it was found that the presence of the above discrepancies and problems has a significant impact on the level of quality management of services provided by railway companies. At the current stage of the integration of the railway industry of Ukraine into the EU transport system, it is necessary to clearly understand the problems that arise in the process. Only then it will be possible to resolve the discrepancies between consumer expectations and the actual quality of services they receive. As a result, it was concluded that management of the quality of services of railway transport enterprises should be more effective, using modern methods of comprehensive study of consumer needs and further improvement of the quality of consumer services. This is done to make a profit, increase the social significance of railway transport and gain competitive advantages in the future. [ABSTRACT FROM AUTHOR]

Productive and uninterrupted operation of ports and their provision of value-added services to customers have become necessities brought about by competition. A performance measurement tool is needed in ports to ensure that the service ports provide is uninterrupted and productive. Four basic logistics processes occur in container terminals: ship operations, transportation, storage, and handling of containers in the port area. Thus, this study aims to develop a logistics-oriented decision support model that will function as a decision support tool for port management to help in understanding, analyzing, and evaluating a port's logistics structure and port performance indicators for planning port capacity, increasing port productivity, improving intra-port logistics processes, and predicting a port's future needs. This study offers an extensive analysis of the literature on simulating the processes in container terminals using the bibliometric analysis method. The study preforms an in-depth analysis of 51 studies in the Scopus database. The study also performs co-citation and author-keyword analyses on the obtained data with the help of the software program VOSviewer. The study concludes by presenting authors some recommendations based on the analyzed studies. [ABSTRACT FROM AUTHOR]

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Abstract Agritech uses advanced technologies to boost the efficiency, sustainability, and productivity of farming. The Internet of Things (IoT) in agriculture has brought sensors and networked technology to gather and analyse environmental and crop data, enabling precision farming that optimises resource usage and increases yields. Yet, current agricultural methods suffer from unsecured and decentralised data management, causing inefficiencies and complicating traceability across the supply chain. The integration of IoT with blockchain technology is seen as a promising solution to enhance data‐driven agriculture. Blockchain provides a secure, decentralised, and transparent ledger that enhances data integrity, reduces fraud, and improves traceability, which complements IoT applications. The authors detail the development of an innovative system that orchestrates IoT and blockchain technologies to facilitate the adoption of new technologies in agriculture and overcomes the lacked of comprehensive data connectivity. It outlines a conceptual framework and its preliminary empirical implementation. The system consists of three integrated layers: the IoT layer, which creates digital twins of field crops; the blockchain layer, which secures and manages data from the field and external stakeholders for dynamic applications such as track and tracing; and the orchestration layer, which fuses physical and digital data to optimise business models, enhance supply chain productivity, and support governmental policy‐making, thereby improving field productivity and food sector innovation.