Enhance Customer Service Research Articles

How and why (imagined) online reviews impact frontline retail encounters

This research examines how frontline retail employees respond to customers whom they think might write an online review about their experience. Across six experiments (one field and five online) we show that when employees identify potential online review authors, often by what the customer says or does, it catalyzes them to deliver better service. This ensues because they experience a rise in determination to do well, motivated by the prospect of being associated with a positive review, which they believe will impress the retailer. Thus, they go ‘above and beyond’. However, determination is tempered by two boundary conditions. When employees (i) do not consider that being associated with an online review is beneficial (i.e., not goal relevant) or (ii) feel poorly equipped to serve the customer (i.e., low in self-efficacy), then a better service delivery will not occur. We also show that retailers can enhance customer service through internal championing of the importance of online reviews, so long as this is framed as promotional rather than punitive.

Microsoft Customer Relationship Management for Small and Medium-Sized Enterprises: Challenges and Opportunities

Customer Relationship Management (CRM) platforms are widely used as tools to help organizations streamline processes, develop customer relationships, increase sales, enhance customer service, and boost profitability. Customer acquisition and retention, which are at the core of a CRM’s function, depend on building strong relationships and tracking prospects and customers. However, selecting a suitable platform to accomplish the aforementioned goals is crucial, especially for medium-sized businesses (SMEs). There are great advantages to using CRM for any business, like enhanced contact management, cross-team collaboration, heightened productivity, empowered sales management, accurate sales forecasting, reliable reporting, improved sales metrics, increased customer satisfaction and retention, boosted marketing Return on Investment (ROI), and enriched products and services. This review compares the challenges and opportunities regarding using Microsoft CRM platform and a few other well-known CRM platforms, with an emphasis on SMEs.

Cognitive Software Defined Networking and Network Function Virtualization and Applications

The emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the Internet. Using SDN, network devices can be controlled from a centralized, programmable control plane that is decoupled from their data plane, whereas with NFV, network functions (such as network address translation, firewall, and intrusion detection) can be virtualized instead of being implemented on proprietary hardware. In addition, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key to automating network operations and enhancing customer service. Many of the challenges behind SDN and NFV are currently being investigated in several projects all over the world using AI and ML techniques, such as AI- and software-based networking, autonomic networking, and policy-based network management. Contributions to this Special Issue come from the above areas of research. Following a rigorous review process, four excellent articles were accepted that address and go beyond many of the challenges mentioned above.

Dependable modulation classifier explainer with measurable explainability.

The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI) that plays a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a password over a bandpass frequency range. Our study focuses on modulation classification performed using deep learning method(s), adaptive modulation classification (AMC), which has now become an integral part of a communication system. We propose a dependable modulation classifier explainer (DMCE) that focuses on the explainability of modulation classification. Our study demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We also demonstrate a numeric explainability measurable metric (EMM) to interpret the prediction. In the end, we present a comparative analysis with existing state-of-the-art methods.

Comparative analysis of facial emotion recognition

This paper provides an overview of the phases, methods, and datasets used in modern Facial Emotion Recognition (FER). FER has been a crucial topic in computer vision and Machine Learning (ML) for decades. By using Convolutional Neural Networks (CNN) to recognize facial expressions, valuable insights into people's emotional states can be gained, leading to improved services such as personalized healthcare, enhanced customer service, and more effective marketing. Automated FER can be used in various settings, including healthcare, education, criminal investigations, and Human Robot Interface (HRI). The study includes a comparative analysis of the performance and conclusions of several models such as Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50), MobileNet, Deep CNN and the proposed pretrained VGG 16 architecture. These models can be integrated into different systems for various purposes such as obtaining feedback on products, services, or virtual learning platforms. Ultimately, Facial Emotion Recognition using Convolutional Neural Networks (CNN) can help reduce bias in decision-making processes by providing an unbiased assessment of a person's emotional state.

Relationship between E-CRM, Service Quality, Customer Satisfaction, Trust, and Loyalty in banking Industry

E-CRM strives to enhance customer service, build relationships with customers, and keep key clients. E-CRM deals with technology, people, and processes and with the goal of fostering customer loyalty. This paper aims to investigate the relationship between E-CRM, service quality, customer satisfaction, trust, and loyalty in banking industry. In order to gather sufficient reviews, a literature review was carried out utilizing a number of corresponding publications that were indexed in reliable databases. A model that highlights the relation between E-CRM and customer satisfaction, service quality, trust, and loyalty is also shown in this study. The supervisors of administrative organizations can utilize this research's insights into E-CRM to build client loyalty and increase the revenue and profitability of their firm

The use of ‘ecommerce’ and ‘social commerce in emerging markets’: Omani female millennials’ experiences

Commercial social media (social commerce in emerging market) have increasingly been used in many emerging markets as an evolutionary form of ecommerce. While this trend has recently been examined from an e-vender's perspective, little is known about how customers, specifically female millennials, perceive of their shopping experiences with commercial social media in relation to ecommerce. Thus, this study explores the underlying motivations for using one e-retailing type over the other. We consider the social constructs of trust, customer service quality and web design in the study. Drawing data from 20 in-depth interviews with young Omani women, the study revealed that ecommerce is most preferred to commercial social media. It highlights the role of unethical marketing practices; unsystematic delivery costs; unprofessional customer service quality; underdeveloped local market and uneasiness of commercial social media in influencing the decision-making of the customers to shift to ecommerce. The study further points to the intersection between product's price, quality and information in building trust; the role of professionalism, responsiveness and empathy in enhancing customer service quality; the importance of product quality and variety in the local market; and the easy navigation and online payment of e-retailing platforms. The study bears implications on enhancing the features and services of commercial social media. It also calls for transforming the local market to meet the customers’ needs.

Implementation of Blockchain in the Banking Sector

Blockchain technology is transformative, and is expected to have a massive economic impact similar to the one the Internet has had in the past few decades.With the increasing innovation in technology for modernizing our lives, new ways to make day to day life effortless and quicker have been introduced. Banking industry is one such field that has been majorly impacted by the digital transformations with the aim of providing quicker transactions for enhanced customer services, cost efficiency and security. Banks have been used as the intermediary third party for carrying financial transactions from early history. With the advancements in technology, the banking industry has adopted one such technique to overcome this third-party intervention. Blockchain is a digital technology that offers decentralization, security and transparency of transaction records. Bank transactions are often considered non-transparent, inefficient and expensive, blockchain is a data structure that ensures that all of the above problems are taken care of. Blockchain gained its popularity after the introduction of the first cryptocurrency which is bitcoin. Almost every sector and domain have been trying to work with this technology to solve their complications. And this paper is a brief study of how blockchain works and can be used to revolutionize the banking industry.

Maturity and Value of Big Data in the Telecommunications Sector in the Republic of Yemen from the Prospective of Information Technology Professionals

Big Data (BD) is considered as the most powerful currency and the main driver behind most current technologies such as block chain, the internet of things, and artificial intelligence. The maturity level of a company determines the level of successfulness in BD of that company. There is a big impact of BD in the telecommunications industry since it contributes heavily in: optimizing network services and usages, improving security, and enhancing customer service and experience. This study assesses the perceptions held by information technology professionals regarding the maturity and value of BD in the telecommunications sector in The Republic of Yemen. The questionnaire was developed, validity tests were conducted (virtual, discriminatory, formative, and subjective) and a reliability test was conducted on it to ensure its ability to measure the reality of information technology Professionals' perceptions regarding the maturity and value of big data. The study population consisted of (213) Professionals in Information Technology Professionals in the Telecommunications Sector in the Republic of Yemen. The sample was selected using the comprehensive enumeration method, and the analysis was done on all telecommunications companies in Yemen, which consists of 6 companies. The study shows the results, the most important of which is a statistically significant effect between BD maturity and BD value in the telecommunications sector in the Republic of Yemen with a percentage (59.6%).

Procurement Volume Prediction of Cross-Border E-Commerce Platform Based on BP-NN

For logistics management, predictive analysis has never been more crucial than it is now, thanks to the vast number of deal data generated every second by electronic commerce. In an effort to enhance customer service and supply control, e-commerce businesses are progressively utilizing machine learning technologies to enhance projections. The back-propagation neural network (BP-NN) model is used to develop a C-A-BP forecasting model that considers commodity sales characteristics and the data series’ trend. In order to predict each cluster, a C-BP-NN model is first created, adding sales information as influencing elements into the C-BP-NN model. An A-BP-NN model is used in combination with the ARIMA that is employed for the linear component. These two forecasting models are merged to provide the final results. By comparing the results of the ARIMA, BP-NN, A-BP-NN, and C-BP-NN using the information provided by Jollychic’s cross-border platform, the A-C-BPNN was shown to be the best.

Enhancing Customer Service Experience: A Case of WBL (India)

Sitting at his office, Mr Gangwani was anxiously flipping through the reports scattered on the desk. The continuously growing customer complaints and substandard service delivery ratings were not only disturbing him but affecting the company’s business also, at large. Despite several measures such as organizational restructuring, conduction of employees’ motivation and training sessions, implementation of a state-of-the-art enterprise resource planning (ERP) solution taken by him to streamline organizational processes, etc., the bank’s customers were not satisfied with the service delivery and overall experience. This case presents the deteriorating customer service experience situation at Wealthworth Bank Limited (WBL) and put forward a framework (HEART parameters) as a possible solution to enhance customers’ service experience. It also questions the interoperability of the ‘HEART’ approach across WBL’s various foreign centres.

Service Quality Perception in Private Banks: A Study with Special Reference to Karur Vysya Bank Ltd

Nowadays private sector banks are giving new dimension of the banking chapter. Private banks are quickly adopting the technology for banking operations and customer satisfaction. Because of the heavy competition in the banking industry, private banks are forced to do the technology advancements and customer satisfaction. The researcher tried to find out the Karur Vysya bank customers expectation through a well-defined interview schedule. The challenging business process in the budgetary service pressurized banks to introduce an alternative delivery channel to pull customers and improve customers perception. Customer satisfaction and customer retention are increasingly developing to key success elements in banking. Technology specifically has been increasingly employed in service association to enhance customer service quality and delivery, reduce costs, and standardize core service offerings. The banking scenario in India is the post liberalization and deregulated environment has witnessed sweeping changes. The tremendous advances in technology and the aggressive imbuement of data technology had gotten an outlook change in banking operations. Technology has emerged as a strategic resource for achieving higher efficiency. Another strategic challenge confronting banking establishment today is developing and changing needs and expectation of customers in tandem with increased education levels and developing wealth consumers are became increasingly decreasing and have become more involved in their monetary decisions. Thus, they are demanding a worldly range of items and services at more competitive prices through more efficient and convenient channels.

Artificial Intelligence's Role in the Growth of Entrepreneurship Development

Innovation is also an essential part of a business start-up process. Many new ventures grow around a creative idea or a decision to put a new company model on the market. Spontaneously, some revolutionary innovations arise, but most commonly, they emerge out of a deliberately designed and organised phase of the invention. They will need a detailed strategic strategy to bring one's designs to fruition and funding to help users design, test, and sell the inventions. To minimise operating costs, improve performance, increase sales and enhance customer service, many organisations use artificial intelligence (AI) technologies. Businesses should consider putting the entire range of intelligent technology into their systems and goods, including artificial learning, natural language processing and more, for the maximum gain. Also, companies that are new to AI, though, will reap big rewards.

The Role of Salesforce CRM in Enhancing Customer Support and Service Operations

The Role of Salesforce CRM in Enhancing Customer Support and Service Operations

Emotional Intelligence in Air Transport: Case study of Flight Attendants in Thailand

Emotional Intelligence is vital in organizations. The measurement of Emotional Intelligence has been widely used in many ways. This research focused on the appropriate use of Emotional Intelligence in the Airline Business. The authors are interested in the implication of Emotional Intelligence of staff towards improving the service. Flight attendant is the focused group for the researchers to discover the emotional intelligence of service providers. The findings of the study demonstrated a relationship between emotional intelligence and customers satisfaction is significant. The author recommends skills that are vital for service providers in the air transport. The two characters are caring and respect. The air transport is an important sector for the contributions of the revenue for the country. Enhancing customer service with the tools of Emotional Intelligence would enhance the economic growth of the country.

Transportation Makes Modern Retail “Click”: Research Questions for the Future

Abstract The modern retail environment is evolving rapidly under the confluence of technological advancement, environmental uncertainty, and general market dynamism. We summarize and synthesize the studies in this special-topic issue to illustrate how transportation capabilities enable members of the modern retail supply chain to adopt new business models, enhance lifestyles of community members, and contribute to corporate social responsibility. Further, we propose extensions to the studies published in this issue to further examine the future of transportation in retail. Notably, we call for future studies to explore how retail supply chain members may (1) develop new transportation and distribution capabilities by overcoming technical debt associated with specialized but abandoned assets; (2) leverage secondary markets to develop a closed-loop supply chain to maximize value and minimize waste; and (3) use enhanced transportation transparency and visibility to enhance customer service and sustainability efforts in retail supply chains.

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric‐Mechanical Equipment Industry

Electric‐mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high‐profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high‐end equipment manufacturers using mass customization.

A VR-Enabled Chatbot Supporting Design and Manufacturing of Large and Complex Power Transformers

Virtual reality (VR) immersive technology allows users to experience enhanced reality using human–computer interfaces (HCI). Many systems have implemented VR with improved HCI to provide strategic market advantages for industry and engineering applications. An intelligent chatbot is a conversational system capable of natural language communication allowing users to ask questions and receive answers online to enhance customer services. This research develops and implements a system framework for a VR-enabled large industrial power transformer mass-customization chatbot. The research collected 1272 frequently asked questions (FAQs) from a power transformer manufacturers’ knowledge base that is used for question matching and answer retrieval. More than 1.2 million Wikipedia engineering pages were used to train a word-embedding model for natural language understanding of question intent. The complex engineering questions and answers are integrated with an immersive VR computer human interface. The system enables users to ask questions and receive explicit and detailed answers combined with 3D immersive images of industrial sized power transformer assemblies. The user interfaces can be projected into the VR headwear or computer screen and manipulated with a controller. The unique immersive VR consultation chatbot system is to support real-time design consultation for the design and manufacturing of complex power transformers.

Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

PurposeSeveral review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.Design/methodology/approachThe approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.FindingsThe review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.Practical implicationsThis review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.Originality/valueThis paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Assessing operational efficiency and the use of strategic capabilities in the rail transport industry in Namibia

This paper assessed the operational efficiency as well as the use of valuable resources and strategic capabilities within the rail transport industry/sector in Namibia, with specific reference to TransNamib Holdings Limited. This paper also focuses on the long-term direction of the rail transport sector as well as the causes of poor performance in the sector. The researchers analysed the questionnaires using descriptive statistics. The researchers explored and presented the individual variables to show specific values, highest and lowest values, trends, propositions and distribution values. The major findings from the data collected revealed that available resources and strategic capabilities are effectively utilized to optimize operational efficiency and that the top management does not pay much attention to development of strategic capabilities, customer value, new strategies, corporate governance, risk management, communication, benchmarking, knowledge management etc. It is also evident from that findings that the main causes of poor performance are the lack of strategic direction and long-term objectives, decline in volumes transported, lack of new technologies to create value, lack of funding or financial assistance from the shareholders, lack of performance agreements and measures as well as the aging and poor rail transport infrastructure. It is recommendable that TransNamib Holdings Limited develop strategies to turn around the current situation to transform the company into self-sustaining and profitable organization. The rail transport sector needs restructuring to streamline the business operations, with more focus on core business, secure funding, enhanced customer services, enhance value proposition for road-to-rail strategy, and enhancing stakeholder and shareholder relations.