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Despite scholarly critiques, use of the Net Promoter Score (NPS®) continues to grow in popularity among firms. Even researchers who criticize NPS recognize its potential for predicting desirable customer behaviors and revenue growth. However, a firm's goal in measuring NPS is not to simply assess it, but to be empowered to improve that score. This goal begs an important question: Does a "net promoter" study provide the insights that a firm needs to improve their NPS? The critical, but untested, assumption in NPS use is that the insights gained from open-ended customer comments in a net promoter study can be used to set improvement priorities that will increase future NPS likely-to-recommend ratings. Through two distinct studies, one in a business-to-business context with auto dealership decision-makers and one in a business-to-consumer context with vehicle repair customers, we investigate this assumption. Our results reveal that the critical untested assumption of a net promoter study is questionable. The convergence is low to moderate between open-ended priorities from a net promoter study and predictors of the standard 'likely-to-recommend' NPS question. We also find that the convergence between customers' open-ended priorities and their stated and derived priorities from closed-ended responses is higher for NPS detractors and passives than promoters, but it is still only moderate. The strength of this convergence of priorities is also impacted by the wording of the questions used to elicit customers' open-ended priorities. Firms using open-ended comments to set priorities should ask customers, especially detractors, to identify areas for improvement. In addition, they should supplement open-ended customer feedback with closed-ended questions to get customers' performance ratings in relation to their specific needs. [ABSTRACT FROM AUTHOR]

Despite the growing diversity of customer needs, little empirical evidence would guide managers to leverage customer need diversity (CND) for radical innovation. Building on the knowledge-based view and dynamic capabilities, the authors explore relationships among CND, strategic flexibility, big data analytics capabilities, and radical innovation performance. With 308 survey responses from innovation managers, the authors find that CND impacts radical innovation performance via (1) mediation through strategic flexibility and (2) moderation by volume, variety, and velocity capabilities associated with big data analytics. The moderation effects of volume and velocity capabilities are positive and negative simultaneously, depending on the purpose for which CND is utilised. Therefore, to leverage customer need diversity, firms need to build strategic flexibility and exercise caution when utilising big data analytics capabilities. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study is to understand the relationship between aligning with customer needs in the context of shaping corporate social responsibility for companies operating in interorganizational networks and the benefits of improved access to market information, strengthening market position, and increasing the flexibility of business operations as advantages gained from mutual connections and relationships with market partners. Design/methodology/approach: Based on literature studies and statistical analysis using the Kendall rank correlation and Kruskal-Wallis test, we analyze the relationship between adapting to customer needs and other benefits from mutual connections and relationships with market partners like improving access to market information, strengthening market position and increasing the flexibility of business activity. Findings: The results suggest that in the case of enterprises operating in inter-organizational networks, better adaptation to customer needs is accompanied by strengthening the market position and providing better access to market information; however, it does not necessarily lead to greater flexibility of business activities. Research limitations/implications: A clear implication of the theoretical assumptions and conducted research is that although it is believed that in the case of enterprises operating in inter-organizational networks, better adaptation to customer needs leads to greater flexibility of operations in exceptional situations, for example, in micro-entities, where the ability to current settlement of liabilities and obtaining funds is limited, there will not necessarily be an increase in the flexibility of their business, which, however, does not reduce the possibility of shaping their social responsibility. Practical implications: This study supports the current view that understanding customer needs shapes socially responsible enterprises operating in inter-organizational networks in terms of strengthening market position and improving access to market information. Moreover, the multi-aspect nature of understanding customer needs in the context of shaping corporate social responsibility and the interdisciplinary nature of the considerations create grounds for further research and exploration in this area. Originality/value: By focusing on the synergy of benefits generated by cooperation in interorganizational networks, the article contributes to research on understanding how mutual connections and relationships with market partners allow for adaptation to customer needs that may shape corporate social responsibility. [ABSTRACT FROM AUTHOR]

The rapid growth, large volume, and easy access to publicly available netnographic data have increased the need for understanding its potential in leveraging innovation. At the heart of quality management is a focus on customers and their needs, driving the innovation of products and services. In this study, we systematically compare netnography and focus groups as research methods regarding their ability to capture various types of customer needs. This study makes four valuable contributions to quality management theory: (1) netnography and focus groups captures similar customer needs, (2) netnography provides clear need statements, which are rich and overrepresented compared to focus groups, (3) both netnography and focus groups are vulnerable to groupthink, where customers prioritize consensus; however, this effect is more evident in netnography, (4) netnography captures more need statements about current product and service usage, while focus groups focus more on past product and service usage. [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.

The article deals with specific features of quality management at Russian enterprises in conditions of intensive import-substitution connected with introduction of economic sanctions against our country. The article briefly studies preconditions of imposing economic sanctions, analyzes specificity of conducting import-substitution in the Russian Federation in recent years, identifies key problems in the field of quality management in respect of importsubstituted products and provides recommendations aimed at raising the efficiency of quality management at Russian enterprises in the current situation. In the field of import-substitution we can observe certain positive trends however we face a lot of problems due to withdrawal from Russia and refusal to cooperate on the part of Western companies, which caused the necessity to re-adjust production processes, to alter supply chains and to build renovated systems of product quality management. Provision of high quality of products is one of the most important objectives in new circumstances, but for this end we shall search for new suppliers of high-quality raw materials and components and we need serious changes in business-processes. Current problems in this field include perception of quality management only from instrumental point of view, insufficient attention to customer needs, the absence of clear-cut understanding of benefits provided by executives’ adherence to philosophy of total quality management, difficulties with introduction of process approach to quality management, the absence at the enterprise of orientation to continuous improvement of quality indicators and so on. When these problems are resolved, we can reach success in the field of quality management.

Copyright of Riwayat: Educational Journal of History & Humanities is the property of Riwayat: Educational Journal of History & Humanities 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.)

To support market success, it is important to identify customer needs, and the relation between customer needs and customer purchasing behaviour. This paper provides an overview over existing, already established approaches to determine the relevance of customer benefits. Then, an approach utilising artificial neural networks to correlate the attributes of battery electric vehicles and their sales performance is presented. This approach is discussed in relation to needs expressed by customers in surveys as well as typical user behaviour of passenger cars. It seems that, for example, charging speed of electric vehicles is more important than operational range despite customers regularly expressing operational range as their greatest concern. The presented approach can be integrated into the reference process for developing product profiles and can be coupled with drive system optimisation methods, to consider sales performance alongside vehicle performance, efficiency and costs in the early stage of product generation engineering. [ABSTRACT FROM AUTHOR]

In the age of digital economy, customers actively share their experiences and issues about products via online product reviews. Mining potential product improvement ideas from customer needs could provide valuable insights into new functionality expected by the markets. Numerous studies have attempted to identify customer needs using these reviews, but they paid less attention to the customer’s specific context in which the product was used. This study provides a novel approach for identifying customer needs based on both context information and product functions of target products. The context information and product functions are derived from online product reviews through linguistic pattern mining, whereby the customer needs are determined by the combination of extracted context information and product functions using a semantic embedding method and a clustering approach. A case study on the Amazon-Echo series was conducted to verify the applicability of the proposed approach. Consequently, we identified 1430 different customer needs, which could be used as an input for improving product design. This study is one of the first attempts to integrate context information for identifying customer needs. The proposed approach can be useful in the idea creation process for future product planning and is expected to add new empirical perspective for the e-commerce industry.

Intense market competition can affect customer responses to bank performance. Bank need customer responses to their products or services so that they can be used as material for evaluation and improvement in the future. Customer needs continue to change and increasingly diverse in line with development in the world and technology, so companies must be able to meet the needs desired by customers. The purpose of this research is to determine customer responses to the current Bima Mobile service and customer needs for the availability of Bima Mobile application features. In this research the descriptive statistical measurement used is the mean of customer response data and customer needs which have been arranged in a frequency distribution table. The analysis results show that the lowest indicator on the customer response component is occupied by the feature availabilityr, while the highest is application display. For the customer needs component, the analysis results show that the lowest indicator is the availability of the donation payment feature and the highest is the availability of the electricity payment feature. The company can take steps to follow up on customer responses to the features avaibility indicator that occupies the lowest position, by developing the Bima Mobile application features availability according to customer needs.

One of the traditional tenets of marketing is that managers considering whether to develop and launch a new product should adopt a customer orientation and consider whether the product would satisfy the needs of customers. This research discovers that adopting a customer orientation causes managers to experience undesirable cognitive effects. The authors find that when considering customers' needs during the screening phase of the new product development process, managers often voluntarily engage in mental imagery (i.e., cognitive simulation) that biases their evaluation of a new product idea toward unrealistic optimism—even for a flawed product that would not satisfy customer needs. Furthermore, the authors find that managers who exert greater vigilance to achieve more accurate evaluations of the new product idea are especially vulnerable to the biasing effect, leading to less accurate evaluations. The authors test an analytical technique (i.e., a theory-based approach to analyzing the new product) that successfully allows a manager to adopt a customer orientation without an attendant bias toward optimism. [ABSTRACT FROM AUTHOR]

Acquisition of customer needs usually serves as the basis for the identification of to-be-improved features for the product redesign process. However, the customer's true needs tend to be non-obvious and are difficult to extract from the data source like interviews or market survey. In the era of Big Data, with the advances in e-commerce, the customer's online review has become one of the most important data source to reveal the insight of customer's preference. In this paper, an online-review-based approach is introduced to identify the to-be-improved product features. The product features and corresponding opinions are extracted and reduced based on the semantic similarity. A structured preference model based on the semantic orientation analysis is constructed. A redesign index is subsequently introduced to measure the priority of redesign for each feature, and a target feature selection model is created to identify the to-be-improved features from candidate features considering engineering cost, redesign lead time and technical risk. A case study for smartphones is developed to demonstrate the effectiveness of the developed approach. In the future study, the online reviews may be combined with the traditional survey data to provide a more effective and reliable identification on the to-be-improved product features. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this research is to identify the needs of customers in providing services through electronic channels with a mixed approach. The statistical population includes two groups of managers and experts in the field of electronic banking services, and all customers of Bank Mellat in Ilam city. The sampling of the qualitative part was done in the form of a snowball; and 10 experts and managers were selected, and 384 people were selected in the quantitative part by simple random sampling and through Cochran's formula. The tool for data collection is semi-structured interviews in the qualitative part and researcher-made questionnaires in the quantitative part. To analyze the data, the qualitative part was analyzed by the theme analysis, and the quantitative part by the structural equation model. The results showed that a total of 28 components were identified and extracted in the 5 main categories of service characteristics, service reliability and security, access and service delivery time, understanding and recognition, communication with the customer, and price and cost. Also, the results of the structural equations indicate that the 5 identified categories are among the factors affecting the needs of customers. Extended Abstract Introduction In every industry, in order to respect the customer and create satisfaction, one must move along with the needs and demands of the customers. Since the first Iranian bank started the activity of the army staff payment in a booth until now when a large range of bank branches –Financial and credit institutions - cooperatives and charitable loan funds are operating, all of them have started these activities and creations in order to provide a group of needs of their customers, which according to the diversity of these organizations and the varieties of the service in terms of their customer-oriented statutes, which they have placed at the top of their organization, their activity shows the satisfaction and provision of a group of customers' needs (Ramezani, 2016). The implementation of e-commerce requires the implementation of electronic banking. For this reason, the use of electronic systems in the financial and credit institutions of the world is expanding rapidly, and the number of users of electronic banking services is increasing day by day. What differentiates the services of banks in the use of electronic banking systems from the current methods is the quantitative and qualitative expansion in customer service, in other words, electronic banking gives the customer the possibility to have a wider and more diverse range of services. (Kholosi adab et al., 2017). The current issue is the increasing need of customers to use banking services through electronic channels in order to reduce the effort and increase the opportunity costs of using these services, so providing faster services through information technology, which is currently the top priority of all Banks of the country and the world have been placed in the attention of the majority of customers, because of not paying appropriate attention to it has become a threat to negligence of some customers to the banks. Therefore, using the opinions and demands of customers in providing banking services to them through electronic channels will make the organization successful in the field of electronic banking and increase the number of bank customers due to the fulfillment of their demands. It can be said that creating trust, satisfaction, and usefulness and saving time are among the cases and scales of using electronic banking tools that should be cultured in its growth and promotion. In fact, the main problem of this research is to identify the needs of customers in providing services through electronic channels with a mixed approach. Theoretical framework The banking industry and providing financial and monetary services is one of the important service sectors in every country. The process of privatization and the entry of private banks in Iran for the past two decades has drastically transformed this sector and has led to intense competition in providing better and faster banking services in order to attract the maximum number of customers (Shayestehfar, & Yazdani, 2018).. Electronic banking services are a general concept in the digital development of banking services and the use of services such as mobile banking and internet banking, and the concept of electronic banking and its functions are not yet fully understood by many people, and the necessity of developing electronic banking services in the banking industry is undeniable. (Shaikh, & Karjaluoto, 2015) Kurznack et all, (2021) state that companies must reconsider the way they create value and develop their business in order to thrive in the unstable and uncertain business environment of the future. For this purpose, they developed a model of long-term value creation that supports companies in creating long-term value and determining their strategies based on this, and banks and financial institutions can use this model to evaluate the justification of their investments and loans in the future. Kaur et all, (2020) also investigated the effect of electronic service quality on the satisfaction of commercial bank customers of the Republic of Botswana; they have emphasized on the effect of aspects such as system availability, execution and privacy as components of electronic service quality of ATMs, on commercial banking customer satisfaction. Research methodology The current research is developmental-applicative in terms of its purpose, and a descriptive research of an exploratory type in terms of strategy. In terms of the type of data, the current research is classified in the field of mixed research (qualitative-quantitative). The statistical population of the research in the qualitative part includes experts and managers in the field of electronic banking services. In order to identify experts to participate in the research process, the purposeful sampling method available by the snowball method has been used, and by using this method, a statistical sample of 10 people was selected in the qualitative section and their information was used in the research process. In the quantitative part, the statistical population includes all the customers of Mellat Bank in the city of Ilam, which has been determined as a sample of 384 people using the simple random sampling method and using the Cochran formula method for the unlimited population. Quantitative data was collected through a researcher-made questionnaire and data analysis was done through structural equations. Therefore, the quantitative phase of the research is implemented according to the results of the qualitative phase. Research findings In this research, first by using theme analysis and through interviews with experts in the field of electronic banking, the needs of customers have been identified in 5 categories as the main components in this research: service characteristics, reliability, access and service delivery time, understanding and knowledge of customer relationship, and price and cost, then after identifying the components, the effects of these components were investigated using smart pls software, and the results of the structural equations indicated that the access and service delivery time with the coefficient Beta 0.374 and T coefficient 151.7 is one of the needs of customers in providing services through electronic channels. Also, the results show that cost price with a beta coefficient of 0.142 and T coefficient of 3.799, reliability with a beta coefficient of 0.558 and a T coefficient of 8.275, service characteristics with a beta coefficient of 0.127 and a T coefficient of 2.883, and understanding customer communication with a beta coefficient of 0.183 and a T coefficient of 5.499 are other needs of customers in providing services through electronic channels. Conclusion The current research was conducted with the aim of identifying the needs of customers in providing services through electronic channels with a mixed approach (case study: Bank Mellat customers in Ilam city). The components identified in this research with the influential components investigated in studies price component (Amiri et.al, 2020), customer perception component (Abualsauod and Othman, 2019), customer perception components), Customer relationship (Vafa et.al, 2019, customer understanding component (Farrokhi and Teymourpour, 2017), customer relationship components and service quality convergence (Hakimi et.al, 2019), and is divergent with the results obtained from studies (Yasin et.all, 2020) and (Rejaobelina et. al, 2018). According to the results of the research, it is suggested that banks can help the customer's sense of trust by providing additional and complementary services. When an additional service beyond the current level of service is offered to customers, it creates a feeling of being noticed in the bank customer and leads to an increase in his level of trust in the bank. Banks can develop the enthusiasm of customers to use these software by designing user-friendly banks and simplicity in performing various banking processes in the relevant software. Include customers in the design of software programs and other banking services and create offers that not only identify with the customer, but also demonstrate that you understand them.

The article analyzes the theoretical aspects of travel organization service and its place in the tourism business. The main characteristics of an individual service and cooperation between the provider of the service and the customer were distinguished as well as its importance while highlighting the characteristics of the travel organization service. Quality attributes and methods of the provision of travel organization services were determined. The SERVQUAL method was chosen for the empirical research, which was used to assess customers' perception of the quality of services, and the main criteria for the quality of services were distinguished. On the basis of this method, a questionnaire was drawn up and 105 customers of a particular travel operator as well as all employees of the company were interviewed. The analysis of the survey results made it possible to determine the perception of the quality of the travel operator as the service provider and customer as well as to assess the needs of the customers. Key words: travel organization service, travel operator, customer’ needs, quality of travel operator service.

This case study aims to identify the leadership qualities required to manage the business during a pandemic-like crisis. Kamadgiri Fashion Limited (KFL) a small company incorporated in India in the year 1987, was into the manufacturing of fabric and ready-made apparels. Slowly and steadily, it had established its foothold in the B2B market and also had strategic partnerships with large FMCG companies in the country. During COVID-19 lockdown, the company had to completely stop its manufacturing process. Demand suddenly dropped and the management knew that if this continued, they would not be able to sustain the business. They wanted to convert this adversity into an opportunity. With the help of technological innovation, they started manufacturing bacteria- and virus-resistant masks under the brand name Zero Risque. The company always wanted to be a part of the solution to the pandemic problem, and they could do it with Zero Risque. Decisions were taken quickly to buy the technology, manufacture the product according to the customer needs and market it. They became the first company headquartered in India, to sell these masks in the country. With daily sales almost doubling after few months, the company gradually could overcome the crisis. [ABSTRACT FROM AUTHOR]

This paper proposes a Quality Function Deployment (QFD) based framework to identify the needs of stakeholders and define the engineering characteristics of a powered prosthetic knee. QFD is extensively used as an effective design method for investigating customer needs and translating them into specific design characteristics to generate products/services to meet the needs. Results demonstrate that the proposed framework provides a better understanding of user needs and engineering characteristics in the House of Quality (HOQ), the primary tool of QFD. It is also found that QFD is a useful design tool for designing powered prosthetic knee because QFD can identify correlations between needs (performance, feature, and appearance) and engineering characteristics (Mechanics, Actuator, Sensors, Recognition, Control, Mass, Dimension, Operating Time, and Capability). [ABSTRACT FROM AUTHOR]

Value analysis methodology uses a structured and methodical job plan contemplating several steps to ensure success in its application. This paper intends to perspective the potential of value analysis application, namely in the furniture industry. To draw conclusions of this potential a case study was developed at an IKEA factory in Portugal. To characterize the customer needs in a clear language and to quantify the value of a product (a bookcase) produced in this factory, a value analysis study was developed. Functional analysis is the most known and distinctive phase of value analysis and is, nowadays, an autonomous tool. With this functional analysis, the authors intend to describe the functions that the product performs, to provide a better knowledge of the product, to evaluate the degree of satisfaction of the product through its functional performance and to facilitate the search for alternative solutions. The results obtained allowed us to reach some important conclusions about the application of value analysis in this type of industry and can be an initial step to promote the use of this tool in this kind of industry.

In view of global changes that took place in the last decade in consumer behavior, competition rate and economy in general conditions of doing business are changing. Trade organizations have to search for new ways of raising efficiency in their economic activity. The article analyzes the fast growing internet-trade market. It studies the practice of introducing category management methodology in the internetshop, which works on pets’ goods market of the Ural federal district. During the research the following methods were used: system approach, comparative analysis, synthesis, economic modeling, as well as empiric and expert-analytical and statistic methods of economic analysis. The author prepared recommendations aimed at improvement of product range policy and demonstrated a great potential of the internet-shop, which could help develop strategy and tactics of product sub-categories being analyzed and forecast planned economic indicators. The present research is important for Russian trade companies, especially of the regional level. The author came to the conclusion that in case of adequate planning and product range management category management would provide an opportunity to improve economic figures of the company. Competitiveness of the company could be raised at the expense of meeting customers’ needs taking into account their requests and expectations.

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL 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.)

For engineering design to be successful, it is essential to understand customer experience and identify customer needs. However, it is challenging to understand customers, especially those from different national cultures. The empathy literature suggests that having similar experiences to another person can help understand them better. This study adopts an empathy measure from psychology for use in a project where designers attempt to understand customers' driving experiences in different countries and identify their needs for detecting road hazards. We quantify designers' empathic accuracy and the correctness of their rating of customers' emotional tone. The results show that national cultural differences significantly affect the accuracy of designers' empathic understanding but do not impact their understanding of customers' emotional tone. [ABSTRACT FROM AUTHOR]

Salespeople make two types of judgments about customers in face-to-face interactions: those that are more intuitive and those that are more deliberative. The authors evaluate the influence of accurate intuitive and deliberative judgments on the performance of salespeople. To evaluate this influence, the authors employ matched survey, observational, and objective field data obtained before, during, and after salesperson--customer interactions. The results reveal that accurate intuitive judgments improve selling performance by enabling more appropriate initial sales strategies. These judgments not only help increase the effectiveness of salespeople's selling efforts but also reduce the amount of selling time, resulting in improved selling efficiency. However, performance is compromised when inaccurate deliberative judgments follow accurate intuitive judgments. The findings also identify different antecedents to judgment accuracy. Intuitive accuracy is influenced by domain-specific experience, similarity to the customer, and empathy for the customer, whereas listening skills and customer orientation influence deliberative accuracy. [ABSTRACT FROM AUTHOR]

Overview: B2B companies are an important part of the global economy, and traditionally they have relied on technological capabilities to compete. Currently, the B2B sector is under increasing pressure, as many B2B companies no longer have a technological advantage, and they also face commoditization. Companies see innovation as a way to compete against these challenges. Although product and process innovation are important in B2B markets, companies need other forms of innovation such as service and business model innovation to create a differentiated offering. B2B managers' views on the value of market research vary widely, and many are unaware that sophisticated techniques such as ethnography can effectively identify B2B customer needs. Our Commoditization-Innovativeness Matrix is a diagnosis tool that enables B2B companies to identify actions that can counter commoditization. [ABSTRACT FROM AUTHOR]

Determining the importance values of service features is necessary to prioritize the points in service quality management and improvement. Existing studies have used linearly additive relationship models to estimate service feature importance, such as linear and logistic regression. This traditional approach is interpretable but often limited in terms of model fitness and prediction performance. Meanwhile, modern advanced machine learning models provide high fitness and performance but often lack interpretability. Thus, to achieve both reliable prediction and interpretation, we propose a systematic framework for estimating the importance of service features using online review mining with interpretable machine learning. An interpretable machine learning-based method is proposed to estimate the importance values of features by applying the shapley additive global importance metric to the highest-performance prediction model. We validate the superiority of our framework over existing methods through a case study on the global importance estimation of hotel service features in Singapore. To facilitate additional applications, we offer the implementation code of our work at https://github.com/JK-SHIN-PG/OnReviewServImprovement. • Identifying service features and estimating their importance are crucial for service management. • We propose a framework for estimating the importance of service features using online review mining with IML. • LDA and VADER are utilized to service feature extraction and sentiment analysis, respectively. • SAGE is employed in the proposed framework for estimating the globally important features. • Importance of hotel service features in Singapore is estimated from 32,044 online reviews. [ABSTRACT FROM AUTHOR]

In the world of fast fashion, the focus isn't on price competition any more, and the efforts of all of the subjects in the supply chain are directed toward creating as many product collections in as little time possible (Birtwistle, Fiorito, Siddiqui, 2003). Because of this, fashion companies use project management and strategies such as agile supply chains and quick response systems, insuring that they can meet customers' wants, flexibility, and easier communication in the process which begins with the idea about creating a certain product and ends with this product on shelves, available to the customers in the retail market. This paper intends to show how the fashion industry has changed in the past 30 years, and in which ways it has directed its efforts to more agile practices in order for companies to be competitive in this fast-changing market.

With increasing concerns on customer needs in today’s competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano’s model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano’s model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano’s model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano’s model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach. [ABSTRACT FROM PUBLISHER]

Copyright of Transfer: Zeitschrift für Kommunikation & Markenmanagement is the property of Deutsche Werbewissenschaftliche Gesellschaft 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.)

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers' unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances. [ABSTRACT FROM AUTHOR]

Nowadays, more and more manufacturers bundle products and services together as product service systems (PSSs) to create more values for customers and gain competitive advantages for themselves. To achieve desired benefits and customer satisfaction, PSSs are usually configured by selecting product components and service modules from a predefined component and service library according to some constraints and customer needs. In this paper, an approach for PSS configuration is proposed in which customer needs are firstly divided as functional needs and perception needs which are generally expressed by customers through their own words. And then, the representative descriptions of perception needs are extracted by factor analysis. Based on it, a multiclass support vector machine model is built for configurating a specific PSS that meets customer needs. Finally, weighbridge is used as an example to validate the proposed approach and the results show that it is effective. [ABSTRACT FROM AUTHOR]

This study focuses on improving multifunctional product development. Instead of face-to-face or other survey methods, we used text mining of online reviews to confirm which characteristics consumers prefer. The reference probability (importance), and the difference between positive and negative opinions (satisfaction) were indexed. By linking "importance" and "satisfaction" with a product's quantitative performance, the correlation between satisfaction and quantitative performance was confirmed, and the method of setting a product's design requirements was presented. To verify the validity of the method, we used the subcompact SUV (Sports Utility Vehicle) market in South Korea as a case study. The average importance and satisfaction with each performance aspect of the cars in the market was extracted, and the successful entry of new products in the market, which reflects these market characteristics, was confirmed. The proposed methodology is meaningful in that it reduces the risk (biased, inefficient) of existing consumer survey methods by utilizing big data to identify consumer preferences. Companies can use these findings during the product development process to improve customer satisfaction. This study improves product development methods by combining them with the latest advances in big data-related technologies. [ABSTRACT FROM AUTHOR]

The present study focused on analyzing service quality as a key element to meet social responsibility in small and medium size enterprises (SMEs). The methodology was descriptive, not experimental, and field based. The population was 50 employees from 50 logistic sector SMEs. The data were collected with questionnaires with Likert alternatives with a reliability of 0.80 and 082. Pearson's method perceived correlation was of 0.025. This means that even if the quality of service is not optimal, they could fulfill their social responsibility. The data were tabulated with descriptive statistics through an efficient process in the quality of service. SMEs assume to fulfill their social responsibility since they offer an efficient and effective service to achieve their objectives. However, it is concluded that social responsibility depends at a very low level on the quality of service offered by SMEs. [ABSTRACT FROM AUTHOR]

Service robotics is currently being established alongside classic industrial robotics. Service robots can be adapted to new tasks with little effort. Nevertheless, there are still only a few service robotics applications in use. The development of the individual components leads to high costs for the users. Therefor the SeRoNet (Service Robotic Network) project addresses the development of a digital platform that brings together the various players in service robotics. In this paper relevant roles are identified, analysed in terms of their gains and losses and finally services are derived and clustered to ensure the successful market launch of the platform. [ABSTRACT FROM AUTHOR]

The process of product design specification is subjective in nature and this motivates the application of approaches and methods to make it explicit. Among the available approaches, the systematization of the development of a single product, modular products, family of products and products for mass customization stands out. Among the methods, the design structure matrix (DSM) is highlighted, as well as the use of networks to represent the variables and their interdependence relations. Representation is very important to increase the cognitive capacity of those involved in the design and to facilitate communication between specialists and non-specialists. The clarification of the knowledge and reasoning of the design increases the complexity of the specification, which needs to be managed. In this work, the method called design structure network (DSN) is proposed, allowing the visualization of the design variables as nodes of a network and the relations of interdependence as links and the specification reasoning can be represented as a path that connects the nodes in a network. For the management of network complexity, ten principles based on cognitive processes are implemented. The DSN method was applied in the geometric specification of surfboard, and the results obtained show the potential of graphical representation of the specification reasoning, as well as the ability to reduce the complexity of the network. [ABSTRACT FROM AUTHOR]

Due to the strong technical and functional similarities of today's consumer products, the emotional and sensory product characteristics become increasingly important in order to stand out from the competition. The objective of this research is to develop a data-driven methodology to systematically integrate customer needs into the product development process. For the comprehensive acquisition of valuable data, various sources of information need to be taken into account. The different sources and data have to be standardized and structured to enable data analytics. This research focuses on Social Media as an important data source to identify customer needs. [ABSTRACT FROM AUTHOR]

One of the key performance indicators of brand success is its value. Brand value is an outcome of brand`s performance in market, and is largely depended from brand`s ability to satisfy certain customer needs. For the greatest success in the world`s market brand should resonate its ability to satisfy some of customer`s most universal needs. In this paper authors strives to find out which of the needs world`s most successful brands are resonating with. Therefore paper goal is to is to determine what customer needs world`s most valuable brands are primarily satisfying. First part of paper authors briefly evaluate Maslow theory of needs. In second part of paper authors identify main challenges of brand valuation, and briefly describe today`s most valuable brands. In third part of paper authors analyzes if resonating certain human need in brand makes it to be more valuable. In last part of paper authors summarizes the main findings and gives recommendations for better marketing practices to other brands whose owners have high market ambitions. In order to attain the paper`s goal, authors will use following research methods: Comparative analysis for comparing brands in different brand rankings; Content analysis for determining what need satisfaction brand advertisements resonate; Data analysis for quantify the results gathered from content analysis

Introduction Discovering and understanding customer needs and expectations are considered as important factors on customer satisfaction and play vital role to maintain the current activity among its competitors, proceeding and obtaining customer satisfaction which are critical factors to design a successful production; thus the successful organizations must meet their needs containing the quality of the products or services to customers. Quality Function Deployment (QFD) is a technique for studying demands and needs of customers which is going to give more emphasis to the customer's interests in this way. The QFD method in general implemented various tools and methods for reaching qualitative goals; but the most important and the main tool of this method is the house of quality diagrams. The Analytic Hierarchy Process (AHP) is a famous and common MADM method based on pair wise comparisons used for determining the priority of understudied factors in various studies until now. With considering effectiveness of QFD method to explicating customer's demands and obtaining customer satisfaction, generally, the researchers followed this question's suite and scientific answer: how can QFD explicate real demands and requirements of customers from tractor final production and what is the prioritization of these demands and requirements in view of customers. Accordingly, the aim of this study was to identify and prioritize the customer requirements of Massey Ferguson (MF 285) tractor production in Iran tractor manufacturing company with t- student statistical test, AHP and QFD methods. Materials and Methods Research method was descriptive and statistical population included all of the tractor customers of Tractor Manufacturing Company in Iran from March 2011 to March 2015. The statistical sample size was 171 which are determined with Cochran index. Moreover, 20 experts' opinion has been considered for determining product's technical requirements. Literature and theoretical bases of this study have been collected with research paper tab and the research data has been collected through four researcher-made questionnaires and interview tools. The questionnaire Type 1, used for determining the most important demands and needs of customers based on five choices Likert scale. The questionnaire Type 2 was for gathering data requirements to hierarchical AHP method, and the questionnaire Type 3 was for doing some evaluation about organization's present situation related with competitor's situation based on customer's demands and needs; and the questionnaire Type 4 had been implemented for finding technical requirement weights respect to customer's demands and needs. The reliability of the type 1, 3 and 4 questionnaires determined by Chronbach's Alpha method; after gathering required data for mentioned statistical test, these questionnaires' reliability rates are obtained: 0.768, 0.784 and 0.793, respectively. As well as, the validity of the questionnaires has been examined with content validity method. In this research, for analyzing the gathered data, while taking into account the different stages of QFD method, t- student statistical test was used for identifing the needs and demands of customers, and AHP was used for determining the priority of needs and demands of customers. Results and Discussion The results of one sample t-test for identifying the customer’s most important demands and needs showed that the factors such as: producing low price tractor, the quality of used auto-parts, sustainability and long-lasting the final production (production life and durability), comfort and peace during work, creating operator cabin, easy access to spare components, the amount of fuel consumption, warranty and maintenance, easy access to official repair stations, technical experts and suitable auto-parts, fast respond of brake system during braking and smooth moving identified the important demands and needs of customers. The results of AHP method for determining primary priorities of perceived customers needs and demands revealed as follows respectively: quality of parts, warranty and maintenance, low price, fuel consumption, comfort and peace, life and durability, smooth moving, fast respond of brakes, creating operator cabin and easy access to spare components. Finally, the main demands and related technical requirements have been identified and prioritized with QFD method; the Final results of customer demands and needs by QFD method revealed this prioritization: Quality of Parts, Warranty And Maintenance, low price, Fuel Consumption, Peace and Comfort, Life and Durability, Smooth Movement and lower engine knocking, Fast Respond of Braking System, Creating Operator Room and Easy Access to Components. Conclusions Without any doubt it is obvious that the obtaining customer satisfaction is the most important strategic tool for having successful and highly developed industry in this era. Knowing the customer demands and needs can lead the organization to enhance competitive advantages. This research showed that how could use structured QFD method for identifying prioritization of tractor customer demands and needs for maintain their satisfaction, and identifying importance of each demands, considering the production techniques.

Configure-to-order (CTO) production is introduced for satisfying various individual customer requirements closely while saving various engineering efforts. In CTO, manufacturing companies modularize their products and customize them according to individual customer needs. A variety of modules and options must be provided for meeting various customer needs accurately. However, the more choices of modules and options are provided, the more customers and manufacturers have difficulties in arranging products which meet customer needs with them. In order that a manufacturer arranges optimal products more effectively for customers by means of CTO production paradigm, product architecture and order receiving procedure must be sophisticatedly designed with considering co-occurrence relations originated from customer needs. This paper proposes a novel method for designing product architecture and receiving order procedure with integrating customer needs, physical functions and entity structure through three design structure matrixes (DSMs) and two domain mapping matrixes (DMMs). The evaluation function that optimizes the degree of coherence between DSMs is formulated with a series of DSMs and DMMs. A simulated annealing based method is introduced to explore optimal modular architectures. A case study of an industrial three-axis linear-type robot is demonstrated. The design result by the proposed method is compared with ones that consider only physical functions and entity structure. Its result shows the validity and promises of the proposed method.

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Value analysis methodology uses a structured and methodical job plan contemplating several steps to ensure success in its application. This paper intends to perspective the potential of value analysis application, namely in the furniture industry. To draw conclusions of this potential a case study was developed at an IKEA factory in Portugal. To characterize the customer needs in a clear language and to quantify the value of a product (a bookcase) produced in this factory, a value analysis study was developed. Functional analysis is the most known and distinctive phase of value analysis and is, nowadays, an autonomous tool. With this functional analysis, the authors intend to describe the functions that the product performs, to provide a better knowledge of the product, to evaluate the degree of satisfaction of the product through its functional performance and to facilitate the search for alternative solutions. The results obtained allowed us to reach some important conclusions about the application of value analysis in this type of industry and can be an initial step to promote the use of this tool in this kind of industry. [ABSTRACT FROM AUTHOR]

'House of Quality' is a conceptual tool for mapping attributes from one phase of the design process to the next. It helps to understand the role of different entities, the general flow and the type of information within the design process. However, there is a drawback with the potential to affect decisions earlier in the design process so that later failures of the product are unlikely to be traced. We discuss these limitations and explore its effect empirically as tested on food consumption at a residential school. Relevant data (primary and secondary) allows for both a qualitative and quantitative analysis. Several quality parameters which affect food consumption are identified for developing the 'House of Quality' through which the main factors leading to the deterioration of quality standard are identified. Descriptive statistics were used to determine the reasons for food wastage. Regression Analysis was used to see the effect of the Food Waste Reduction Practice (FWRP) model developed on the overall result at a residential school Mess in India. [ABSTRACT FROM AUTHOR]

Abstract Knowledge about current user preferences and needs regarding electric vehicles is key for developing convenient solutions in the field of e-mobility. They are decisive for a successful market uptake of electric vehicles. In this article, we analyze German Twitter data as fast and unbiased feedback from customers on current products. We demonstrate how this new data source can be applied in the field of e-mobility. We compare our analysis to the traditional approach of a literature survey. Results show that for the German-speaking region the frequency distribution of needs derived from the literature survey differs significantly from the frequency distribution derived from the sample of tweets. Price-related needs and needs concerning Car characteristics are comparably overrepresented in the literature survey. On the other hand, Charging-related needs in particular as well as needs classified in the society cluster are overrepresented in the Twitter data set. We derive interpretations from these insights and illustrate possibilities for stakeholders and policymakers to increase acceptance. [ABSTRACT FROM AUTHOR]

We evaluate user-generated content as a source of customer needs and propose and test a machine-learning approach for identifying customer needs more efficiently. Firms traditionally rely on interviews and focus groups to identify customer needs for marketing strategy and product development. User-generated content (UGC) is a promising alternative source for identifying customer needs. However, established methods are neither efficient nor effective for large UGC corpora because much content is noninformative or repetitive. We propose a machine-learning approach to facilitate qualitative analysis by selecting content for efficient review. We use a convolutional neural network to filter out noninformative content and cluster dense sentence embeddings to avoid sampling repetitive content. We further address two key questions: Are UGC-based customer needs comparable to interview-based customer needs? Do the machine-learning methods improve customer-need identification? These comparisons are enabled by a custom data set of customer needs for oral care products identified by professional analysts using industry-standard experiential interviews. The analysts also coded 12,000 UGC sentences to identify which previously identified customer needs and/or new customer needs were articulated in each sentence. We show that (1) UGC is at least as valuable as a source of customer needs for product development, likely more valuable, compared with conventional methods, and (2) machine-learning methods improve efficiency of identifying customer needs from UGC (unique customer needs per unit of professional services cost). Data are available at https://doi.org/10.1287/mksc.2018.1123. [ABSTRACT FROM AUTHOR]

To successfully develop a complex product, a firm must answer two critical questions: how to "develop the right product" and how to "develop the product right." Motivated by the real practice of Xiaomi's new product development (NPD) projects, this article responds to these calls in the following ways. To design the right product, using an enhanced PageRank algorithm to investigate customer needs, NPD managers can select appropriate function modules of the new product to meet customers' demand. To develop the new product in the right way, NPD managers should optimize the NPD organization. This article applies the multi-domain matrix (MDM) to identify the technical coordination dependency strength among different teams and then to measure the NPD organization's complexity according to its entropy. By proposing the External Entropy of Cluster (EEC) and Internal Entropy of Cluster (IEC), we develop an entropy-based two-stage clustering criterion of design structure matrix (DSM) to optimize the NPD organization. The first-stage clustering criterion maximizes the added average dependency strength of DSM, and the second-stage clustering criterion minimizes the Weighted Total Entropy, including the IEC and EEC. An industrial example is provided to illustrate the proposed model. The results indicate that the clustered DSM can reduce the organization's complexity. [ABSTRACT FROM AUTHOR]

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers’ unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.

This study focuses on improving multifunctional product development. Instead of face-to-face or other survey methods, we used text mining of online reviews to confirm which characteristics consumers prefer. The reference probability (importance), and the difference between positive and negative opinions (satisfaction) were indexed. By linking “importance” and “satisfaction” with a product’s quantitative performance, the correlation between satisfaction and quantitative performance was confirmed, and the method of setting a product’s design requirements was presented. To verify the validity of the method, we used the subcompact SUV (Sports Utility Vehicle) market in South Korea as a case study. The average importance and satisfaction with each performance aspect of the cars in the market was extracted, and the successful entry of new products in the market, which reflects these market characteristics, was confirmed. The proposed methodology is meaningful in that it reduces the risk (biased, inefficient) of existing consumer survey methods by utilizing big data to identify consumer preferences. Companies can use these findings during the product development process to improve customer satisfaction. This study improves product development methods by combining them with the latest advances in big data-related technologies.