Customer Uncertainty Research Articles

Servitization as a strategy for diffusing radical technologies: An analysis of U.S. top corporate R&D investors

The diffusion of radical technologies poses a significant challenge for firms. While existing studies have mainly concentrated on organizing for radical innovation, less attention has been given to strategies that enable firms to bring these technologies to the product market. In this paper, we argue that servitization – the addition of product-related services to tangible product offerings – can serve as a potential solution, largely due to its ability to reduce customer uncertainty about the nature and utilization of radical technologies and offset adoption costs. However, there are limits to deploying this strategy: developing and implementing service-generating capabilities, together with maintaining and advancing the capability to generate radical technologies, can be costly and risky. Our analysis of 505 top corporate R&D investors from the U.S. manufacturing industry confirms a significant relationship between technological radicalness and servitization, but it is nuanced and depends on the level of financial resources available to firms, known as financial resource slack. More specifically, firms with higher slack have a greater extent of servitization activities when their technologies are more radical. In turn, firms with lower slack have a greater extent of servitization activities when their technologies fall within the middle range of the radicalness spectrum. We use these findings to draw implications for practice and policy.

Customer satisfaction antecedents in uncertain hospitality conditions: an exploratory data mining approach

PurposeInformed by trait and self-determination theories, the present study aims to extend the knowledge regarding the link between customer satisfaction (CS) and its antecedents, including job autonomy (JA), conscientiousness, customer uncertainty (CU) and extra-role customer service (E-RCS) in the hospitality industry.Design/methodology/approachA total of 306 frontline employees were selected from the hotels in North Cyprus, Turkey. Psychometric properties, including the validity and reliability of study variables, were assessed in the first step using confirmatory factor analysis. Then, the data were analyzed utilizing machine learning methods, mainly three exploratory data mining techniques, including lasso regression, decision trees and random forest, as well as partial dependence plots to visualize the role of suggested predictors on the outcome variable.FindingsData mining analysis shows that employees who can modify their job objectives are better equipped to satisfy customers in uncertain situations (JA8). In addition, the findings reveal that employees who believe they work hard to accomplish their personal and organizational goals (CON7) while also having the freedom to decide how to approach their job (JA1) and choose the procedures to utilize (JA2) are more likely to contribute to CS. In general, CS peaked when JA was high, but conscientiousness was moderate, while CU was low.Practical implicationsThis study bridges the gap among various factors at the employee and customer individual, corporate and macro-environmental levels. Hospitality organizations can cultivate a culture of autonomy and independence by promoting open communication and offering growth and development opportunities. This approach enhances conscientious employees’ engagement, leading to exceptional customer service performance, particularly, in uncertain situations.Originality/valueFrom the methodology perspective, this work proposes an opportunity for prospective scientists to broaden the trait and self-determination theories research model by relying on the riches of exploratory techniques without the limits imposed by traditional analytical techniques. Further, this study advances the current knowledge about service agility under uncertainty by extending organizational and service management research to consumer behavior literature.

Use of ICT to implement an active learning strategy in soil mechanics courses at undergraduate level

Nowadays engineers are constantly dealing with more complex problems, uncertainty, incomplete data, and demands of customers, governments, environmentalists, and public. This requires technical skills as well as skills in human relations. So, during their academic background it is necessary to incorporate more skills, social and technological, into their base knowledge. This can be accomplished introducing Information and Communication Technologies (ICT) in Higher Education (HE). Several studies show that the use of ICT in teaching promotes participation, engagement, collaboration, and student interaction, making them more active participants and responsible for their learning. In addition to these advantages, ICT allow to give equal importance to learning processes and to the contents, as the activities offered by ICT allow to the students develop communication skills, teamwork, finding and evaluating information, access, and manipulation of large amounts of data, work with other technologies, update and refine existing skills and knowledge. It was in this context that Guided Exercises emerged. A Guided Exercise permits students to relate models and help them to solve a complex exercise step by step. This strategy was used in two consecutive courses of an undergraduate degree in Civil Engineering, Soil Mechanics I and Soil Mechanics II at the University of Aveiro, Portugal. The results show that students considered the strategy useful for the understanding of the concepts covered in the course. Analysing the students’ academic performance, it can be concluded that those who used this methodology had a better approval ratio. This paper presents data to support these statements.

Decoding the information quantity-quality paradox: How eWOM volume influences consumption value uncertainties

This study explored how the type, quantity, and polarity of information shape customer uncertainty in consumption values, utilizing longitudinal and large-scale user-generated data collected from Yelp. Two types of curvilinear relationships between information quantity and consumer uncertainty were identified. Pattern (a) exhibits an inverted U-shaped trend where uncertainty rises with information volume (under scarcity) but diminishes after a threshold (indicating sufficiency). In contrast, Pattern (b) shows recurrent uncertainty surges after surpassing a volume threshold, signaling information overload. Moreover, the study revealed that the polarity of visual information significantly moderates the impact of information quantity on customer uncertainty. The findings suggest that restaurant managers, within the eWOM context, should tailor information management strategies corresponding to different stages of information diffusion. Lastly, the study underscores the importance of monitoring conflicting information for restaurant managers to effectively mitigate customer uncertainty, emphasizing the utility of the novel big data-based approach to measure customer uncertainty.

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Komunikasi Interpersonal Customer Service Lotte Grosir dalam Melayani Pelanggan di Kota Samarinda

Interpersonal communication plays an important role in a company, one of which is retail, the quality of good interpersonal communication needs to be mastered by customer service because it deals directly with customers, the quality of interpersonal communication is needed to create good service for customers. The purpose of this study was to determine the interpersonal communication process of Lotte Grosir's customer service in serving customers in Samarinda City. The research method used is a qualitative method. Data collection techniques include interviews, observation and documentation. The informants in this study consisted of 3 customer service people, 1 guard manager and 2 customers. The results of this study indicate that there are 6 elements found in the field, namely self-introduction and other individuals from customer service openness to customers and customer service digging into customer backgrounds to reduce uncertainty to customers. Knowing the situation can be seen from the way customer service listens to complaints experienced by customers before providing solutions. Creating and maintaining meaningful relationships is evident in customer service. Providing service to customers in a friendly and courteous manner, not hurting customer feelings, giving trust to customers. Persuasion is seen when customer service provides options for exchanging goods before a refund is made if there is a discrepancy with the customer. Relaxed interactions can be seen from the way customer service makes jokes to customers, but one customer service that lacks initiative tends to wait for customers who ask questions. Helping seen from customer service helps reduce cognitive and behavioral uncertainty in customers in making decisions if customers feel unsure about their choices.

Trust and Buying Intent on Social Media Platforms in Vietnam

Social networking platforms have been developing more and more features in connection and sharing between users, thereby speedily communication activities, promoting and trading goods and services. One of the social networks that has achieved outstanding growth in recent years is Tiktok Shop. Although users can shop easily on this application, they still have certain doubts, stemming from the risks encountered. Trust is considered an essential factor to reduce customer uncertainty and risk when shopping online. This article aims to examine the influence of perceived values on trust and online purchase intention of Vietnamese users on the Tiktok. With the research assumption that factors such as media richness, price fairness, convenience, and seller interaction have an impact on trust, and trust impacts purchase intention on the Tiktok, the study conducted a survey with 506 consumers in Hanoi with a questionnaire adapted from previous research. The results obtained demonstrate that most of these factors affect trust, and trust has a positive impact on users' purchasing intention. From there, the article recommends some management implications for businesses on social networks in general and Tiktok in particular.

Design Android-Based Car Rental Management Application Using Prototype Method

Inayah's car rental is one of the business sectors in the field of transportation services at Pekanbaru, Riau. Inayah car rental carries out management activities for car rental management that can be rented from daily to monthly. Management of rental transactions is currently done manually by recording customer data and storing customer guarantees in the archives that have been provided. Problems arise when the data is lost or damaged while the car is on the customer's side. In addition, other issues arise when a customer cancels suddenly on that day, or the customer's uncertainty about car rental will impact other customers who are serious about renting a car at that time. From these problems, we need a system that can automatically manage, store, and manage car rental transactions using Android. The development method in this study uses the prototype development method. The Prototype is a development method that involves users in system development. Based on the research results, we get a very good interpretation of the standard quality of the ISO 25010 system test, functional, performance, reliability, maintainability, security, usability, and efficiency.

The Effect of Customer Engagement on Purchase Intention in Kimia Farma Services in Surabaya

purchase intention in Kimia Farma services in Surabaya. Data were collected through a survey with 106 respondents. The data used in this study is primary data obtained from online questionnaires. The respondents of this research are members of Kimia Farma who shop in in Kimia Farma. The sampling technique used was non-probability sampling. This research is quantitative research with statistical testing using Structural Equation Modeling (SEM) with Partial Least Square (PLS) is used to perform data analysis. In this study it was found that there is a positive influence between customer engagement and perceived value. There is a positive influence between customer engagement and customer satisfaction. There is a positive influence between customer engagement and purchase intention. There is a positive relationship between perceived value and customer satisfaction. There is a positive influence between perceived value and purchase intention. There is a positive influence between customer satisfaction and purchase intention. There is a negative influence between customer uncertainty and purchase intention.

A Three-Phased Fuzzy Logic Multi-Criteria Decision-Making Model for Evaluating Operation Systems for Smart TVs

Within the competitive global market and fast-advancing technology environment, in order to survive and to succeed, firms need to spontaneously respond to market changes and the uncertainty of customer needs. Therefore, New Product Development (NPD) is extremely important for the success of firms. Artificial Intelligence (AI) has gradually entered people’s lives, and consumer demand for AI products is increasing. Firms need to understand the AI development trend and consider the preferences of consumers for AI-related products under social changes so that suitable consumer AI products can be properly developed. In this study, the evaluation and selection of operation systems for a commercially available AI product (smart TV) is studied, and a Multi-Criteria Decision-Making (MCDM) model for facilitating the selection of the most suitable operation system for product development is constructed. The proposed model consists of three phases: Interpretative Structural Modelling (ISM) to construct a decision-making network, Fuzzy Analytic Network Process (FANP) to obtain the weights of factors, and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to rank the operation systems. The proposed model is applied to select an operation system that companies can use to develop a smart TV. The results show that the proposed model can provide a systematic method that helps companies make appropriate operation system selection decisions.

A multi-layer interactive peak-shaving model considering demand response sensitivity

The development of smart grids allows residential customers to participate in demand response (DR) programs to aid power grid management through HEMS (Home Energy Management Systems), but similar electricity consumption behavior among customers based on time-of-use electricity prices may lead to the problem of peak load shift, also known as peak rebounds. This article proposes a multi-level interactive optimization model considering individual sensitivity for DR. The model consists of community energy aggregators (CEAs), which perform as an intermediate processing layer between customers and power grid. Customer terminals perform energy management for home appliances, electric vehicles, energy storage systems, and renewable energy generation. The scheduling problem is decomposed into smaller parallel decision problems that are easier to solve. Renewable generation especially photovoltaic power generation is predicted and used to mitigate the influence of energy generation uncertainty. By introducing numerical responsiveness of customers, the model deals with uncertainty on the subjective level of customers. As indicated in numerical analyses, the model is a good compromise between stochastic optimization depending on idealized probability models and robust optimization sacrificing cost to meet worst case scenarios. The proposed method was compared with existing optimization-based methods for peak shaving. Compared with coordinated load management, our method reduced the peak load and average cost by 13.36% and 18.96%, respectively. Compared with robust optimization, our method achieved similar effect while handling the uncertainty in customers and PV.

ANALISIS PENGARUH COUNTRY OF ORIGIN DAN COLLECTIVISM TERHADAP PURCHASE INTENTION YANG DIMEDIASI OLEH CUSTOMER UNCERTAINTY

This study aims to find out how much influence Country of Origin and Collectivism have on purchase intention mediated by customer uncertainty by using a case study on local shoes in Indonesia. This study uses a descriptive quantitative research method. In sampling the technique used is a non-probability sampling technique with purposive sampling. For data collection in this study using the technique of distributing questionnaires and collecting secondary data. To calculate the results of the questionnaire distribution, a Likert scale with an interval scale was used. This research model is using path analysis. With data analysis using AMOS. The results showed that Country of Origin and Collectivism had a positive effect on Purchase Intention. Meanwhile, Country of Origin has a negative effect on Purchase Intention with Customer uncertainty as mediation and Collectivism has a negative effect on Purchase Intention with Customer Uncertainty as mediation.

The influence of online customer reviews on two-stage product strategy in a competitive market.

We investigate quality and pricing decisions for two competing firms in an e-marketplace with online customer reviews. Through developing two-stage game-theoretical models and comparing the equilibriums, we examine the optimal choice among different alternative product strategies: static strategy, adjusting the price, adjusting the quality level, and adjusting both the quality and price dynamically. Our results show that the existence of online customer reviews tends to encourage firms to increase quality and charge low prices in the early stage, and decrease quality and raise prices in the later stage. Moreover, firms should choose the optimal product strategies depending on the impact of customers' private assessment of product quality from the product information disclosed by firms on the overall perceived product utility and customer uncertainty about the perceived degree of product fit. After our comparisons, the dual-element dynamic strategy is more likely to outperform other strategies financially. Furthermore, we extend our models to examine how the optimal choice of quality and pricing strategies will change if the competing firms have asymmetric initial online customer reviews. From the extended analysis, a dynamic pricing strategy may generate better financial performance than the dynamic quality strategy, which is different from the finding in the basic scenario. Firms should choose the dual-element dynamic strategy, the dynamic quality strategy, the dual-element dynamic strategy coupled with dynamic pricing, and the dynamic pricing strategy in sequence as the impact of customers' private assessment of product quality on the overall perceived product utility and the weight that the second-stage customers place on their private assessment increase.

An Adaptive Tabu Search Algorithm for Solving the Two-Dimensional Loading Constrained Vehicle Routing Problem with Stochastic Customers

In practical logistic distributions, uncertainties may exist in each distribution process, and sometimes suppliers have to take undesirable measures to deal with the subsequent schedule variances. In light of the uncertainty of customers in logistics distribution and the widely applied two-dimensional loading patterns in transportation, we propose and formulate a two-dimensional loading-constrained vehicle routing problem with stochastic customers (2L-VRPSC), where each customer has a known probability of presence and customers’ demands are a set of non-stackable items. A stochastic modeling platform of 2L-VRPSC is established based on a Monte Carlo simulation and scenario analysis to minimize the expected total transportation cost. To achieve this, an enhanced adaptive tabu search (EATS) algorithm incorporating the multi-order bottom-fill-skyline (MOBFS) packing heuristic is proposed, where the EATS algorithm searches for the optimal routing combination and the MOBFS checks the feasibility of each route and guides the EATS to search for feasible solutions. The widely used two-dimensional loading-constrained vehicle routing problem (2L-VRP) benchmarks under different loading configurations considering items’ sequential and rotation constraints are applied for experiments, which demonstrates the comparable efficiency of the proposed EATS-MOBFS for solving 2L-VRP. Furthermore, the results and analysis of experiments based on the new 2L-VRPSC instances verify the versatility of the proposed solving approach, which is capable of providing more practical solutions to some real-life scenarios with customers’ uncertain information.

Investigating customer uncertainty and satisfaction in the banking industry: the role of organisational silence towards the customer, organisational hypocrisy and corporate social irresponsibility

Investigating customer uncertainty and satisfaction in the banking industry: the role of organisational silence towards the customer, organisational hypocrisy and corporate social irresponsibility

Analysis The Influence of Country of Origin and Brand Image on Purchase Intentions through Customer Uncertainty (Study Case on OPPO Smartphone)

Changes in trade activities have occurred as a result of changing times, and technical improvements have benefited corporations or business players in extending or growing markets that are no longer restricted to a single nation but may penetrate other countries. The emergence of foreign products, such as mobile phones, widen the choices of mobile phones in the Indonesian market. The majority of mobile phone brands purchased by Indonesian consumers are from China, despite the fact that the majority of the world regards China as having product stereotypes or product images that are of poor quality, cheap, and frequently counterfeited, resulting in a discontinuity with the product image they have. This study examines and analyses the effect of country of origin and brand image on purchase intentions through customer uncertainty on OPPO smartphone. The study sample included customer over 18 years old who has purchased and used OPPO smartphone. The sample in this study was 200 respondents. The method used in this study is a structural equation model (SEM) with the help of AMOS software for hypotheses test. The results of this study show country of origin and brand image has a negative effect on customer uncertainty, country of origin and brand image has a positive effect on purchase intention, whereas customer uncertainty has a negative effect on purchase intention. This study significantly extends managerial implications for OPPO to focus heavily to solve customer uncertainty by advertising product performance and giving detailed information about the product (such as label) to lower customer uncertainty and encourage customer to have intention to purchase OPPO smartphone. secondly, to solve customer uncertainty OPPO need to eliminate China’s (OPPO country of origin) stereotype as a country with a poor product.

Individual household demand response potential evaluation and identification based on machine learning algorithms

Modern power systems are facing an increase in the penetration of renewables to achieve carbon neutrality targets in the future. Individual household demand response (DR) is a potential resource to address the fluctuation caused by renewable energy generation. However, the DR potential of residential customers is more challenging to evaluate and identify than the DR at the aggregated level due to the uncertainty in individual customers' electricity consumption behavior. In this study, to address this issue, a DR potential evaluation and identification framework based on machine learning algorithms is proposed. Firstly, the customer's DR capacity under dynamic time-of-use electricity tariffs is carefully calculated. Then several novel indicators are proposed to depict customer's electricity consumption characteristics. Next, some prevalent machine learning methods are leveraged to learn the mapping between the developed indicators and DR capacity. Besides, to further improve the classification accuracy and analyze the performance of the unlabeled samples, the self-training method is applied by using customers' data who are issued normal flat electricity prices. Finally, the proposed framework was applied to a publicly available dataset and the results indicated that the proposed approach reached prospective classification accuracy and proved the validity of the proposed indicators and identification framework.

Being digital and flexible to navigate the storm: How digital transformation enhances supply chain flexibility in turbulent environments

The growing environmental business uncertainties have forced companies to focus on developing more flexible supply chains. Digital transformation has been considered a key means to achieving such flexibility, but the literature lacks empirical evidence about how digital technologies effectively contribute to it. Thus, this study aims to analyze how Smart Supply Chain (i.e., a supply chain enabled by digital transformation) contributes to supply chain flexibility and operational performance in environments surrounded by customer and supplier uncertainty. We adopt the organizational information-processing theory to explain the fit between information needs to reduce these uncertainties through more supply chain flexibility (sourcing, delivery, and manufacturing) and information capabilities provided by three main dimensions of the Smart Supply Chain (digital transformation strategy, digital base technologies, and digital front-end technologies). We relate these information-processing fit between Smart Supply Chain and flexibility with the boundary conditions of environmental uncertainty and operational performance improvements. Such relationships are analyzed through moderation and mediation regression tests based on 379 manufacturing companies surveyed. Our findings show that Smart Supply Chain has a statistical association with operational performance through the sequential mediating role of the three supply chain flexibility dimensions. We also found that environments with high customer uncertainty increase the use of base technologies (IoT, cloud, big data, AI, and blockchain) to reach delivery flexibility and support manufacturing flexibility. When companies face high supplier uncertainty, they use front-end technologies (i.e., robotics, 3D printing, simulation, and augmented reality) to increase sourcing flexibility. We show new advances in supply chain flexibility through digital transformation.

Revenue Mode Innovations for Product-Service Systems: Overcoming the Service Paradox

Industrial manufacturers aim to increase competitiveness by innovating their business model and offer product-service systems (PSS). However, firms often fail to exploit sufficient profits from PSS business model innovation (BMI). The role of revenue models to create and capture val-ue from innovating revenue streams for PSS is often neglected in practice and theory. The innova-tion literature has a profound understanding of the PSS concept and its value proposition design. However, it lacks knowledge and insights on how revenue model innovation (RMI) impacts the success and competitiveness of a PSS business model. From a perspective of the resource-dependence theory (RDT), we assume that RMI is a source of competitive advantage by reducing uncertainty and increasing interdependencies between economic actors of a PSS. Our research ap-plied a qualitative multiple case study with semi-structured interviews of leading PSS providers. We identified three dimensions of RMI-enabled customer value creation and provider value capture, reducing customer uncertainty and increasing dependence according to the RDT. Our research im-plies from the RDT perspective that PSS providers create a joint competitive advantage from RMI by giving additional value to their customers, which supports the power balance between both par-ties by a positive increase in interdependence. Our study contributes to the theoretical understanding of the role of RMI for PSS business model success. The study results will extend the theoretical knowledge about successful servitization through PSS and address its problem of exploiting reve-nues from the service business – the service paradox.

Framing Changes of the Value Proposition of Emerging Technologies in a B2B Context

ABSTRACT Purpose Although the literature acknowledges the importance of value proposition change, existing research on how the value proposition can change remains relatively limited. The aim of the study was to develop a framework to explore how the value proposition evolves over time in the case of emerging technologies. Methodology/approach Based on a single case and a processual approach, the longitudinal research design tracks changes in the value proposition over a 25-year period. Findings The study provides a nuanced account of how framing of the value proposition shifted from vision to network and finally to usage through synergetic relationships with customers as the firm moved from startup to IPO and ultimately to public company. On this view, the value proposition emerges as a dynamic communication process that reduces customer uncertainty about the value of new technologies, leveraging company’s resources and competencies. Research implications The findings confirm that value proposition change can be understood as a dynamic communication process that can reduce uncertainty about new technologies and highlights the role of vision in guiding the overall evolution of the value proposition over time, including networking and usage. Practical implications The study confirms the importance of reframing the value proposition over time to address customer uncertainty about the value of new technologies, enabling companies to influence expectations by making certain benefits salient. The study also confirms the importance of adopting a proactive approach to value proposition change. Originality The study’s primary contribution is the development of a framework for exploring value proposition change in emerging technologies in terms of three distinct frames: vision, network, and usage.