Kano-sarnase mudeli kasutamine avatud innovatsiooni saavutamiseks nõuete analüüsi protsessis

Kui viiakse läbi nõuete analüüsi (inglise k Requirements Engineering, lühend RE), siis sageli järjestatakse nõuded nende olulisuse alusel (inglise k requirements prioritization), et saada selgust, milliste välja pakutud nõuetega funktsioon peaks tarkvaral olemas olema, seega sõltub tarkvara analüüsist tarkvara majandusliku väärtuse suurendamisega seotud otsuste tegemine. Tänapäeval arenevad tooted väga kiiresti ning ka nõuete olulisuse alusel järjestamine (inglise k requirements prioritization) on muutunud kiiremaks. Ettevõtted sooviksid saada kasutajatelt kiiret tagasisidet selle kohta, mis peaks olema järgmises mudelis olemas. Üks häid lahendusi sellele on Kano mudel (inglise k Kano model). Kano mudel selgitab välja kasutajate rahulolu ja toodete tunnuste vahelise suhte. See meetod liigitab kasutajate eelistused nende tähtsuse järjekorras, seega toetab see ka nõuete olulisuse järjekorra moodustamist. Aga Kano mudeli rakendamine on kallis ja aeganõudev ning seda ei saa kiiresti korrata. Veelgi enam – see mudel on keeruline väikeste ettevõtete jaoks, sest neil ei tarvitse olla piisavalt rahalisi jm vahendeid, et kasutajatega ühendust võtta ja neid intervjueerida. See omakorda paneb väikesed ettevõtted, eriti just idufirmad, ebavõrdsesse olukorda suurte ettevõtetega. Et sellele probleemile lahendust leida ja Kano mudeli kasutuselevõttu lihtsamaks ning odavamaks teha, arvame, et Kano mudelit tuleks arendada kahel viisil. Esiteks tuleks kasutada tasuta võrgus leiduvaid kirjalikke andmeid, mida saaks asendada intervjueeritavatelt kogutud vastustega. Teiseks – selleks, et hakkama saada võrgust kogutud kirjalike andmete suure mahuga, ning et kaasa aidata korrapärastele analüüsidele, peaks andmete analüüsimine olema automaatne. Selle uurimuse eesmärk on välja pakkuda meetodeid, et kasutajate avamusi, mis on võrgus saadavatest vabadest allikatest kogutud, (semi-)automaatselt liigitada, ja seda selleks, et aidata otsustajatel otsustada, millised tarkvara nõuded järgmises mudelis kindlasti olemas peaksid olema. Et seda uurimuse eesmärki saavutada, pakume me välja avatud innovatsiooni nõuete analüüsi (OIRE) meetodi, mille abil saavad tarkvarafirmad parema ülevaate kasutajate vajadustest ja sellest, kuivõrd rahul on nad olemasolevate toodetega.When Requirements Engineering (RE) is applied, requirements analysis is often used to determine which candidate requirements of a feature should be included in a software release. This plays a crucial role in the decisions made to increase the economic value of software. Nowadays, products evolve fast, and the process of requirements prioritization is becoming shorter as well. Companies benefit from receiving quick feedback from end users about what should be included in subsequent releases. One effective approach supporting requirements prioritization is the Kano model. The Kano model defines the relationship between user satisfaction and product features. It is a method used to classify user preferences according to their importance, and in doing so, supports requirements prioritization. However, implementing the Kano model is costly and time-consuming, and the application of the Kano model cannot be repeated quickly. Moreover, this is even more difficult for small companies because they might not have sufficient funds and resources to contact end users and conduct interviews. This puts small businesses, especially start-ups, at an unfair disadvantage in competing with big companies. To address this problem and make the application of the Kano model simpler, faster, and cheaper, we propose evolving the Kano model in two aspects. First, free online text data should be used to replace responses collected from interviewees. Second, in order to handle the higher amount of data that can be collected from free online text data and in order to facilitate frequent analyses, the data analysis process should be automated. The goal of this research is to propose methods for (semi-)automatically classifying user opinions collected from online open sources (e.g., from online reviews) to help decision-makers decide which software requirements to include in subsequent product versions. To achieve this research goal, we propose the Open Innovation in Requirements Engineering (OIRE) method to help software organizations gain a better understanding of user needs and satisfaction with existing products. A key element of the OIRE method is its Kano-like model. This Kano-like model mimics the traditional Kano model, except that it uses data from online reviews instead of interviews conducted with select focus groups.https://www.ester.ee/record=b527385

EXPERIENTIAL VALUE: A HIERARCHICAL MODEL, THE IMPACT ON E-LOYALTY AND A CUSTOMER TYPOLOGY

The main objective of this study is to empirically test a fourth-order hierarchical model of experiential value in an online book and CD setting. In addition, we provide empirical evidence for the role of hedonic and utilitarian value components in creating attitudinal and behavioral loyalty. Finally, we develop an online customer typology, based on the underlying value sources. Based on a sample of 190 visitors of online book and CD retailers, we used PLS to test a third and fourth order hierarchical model of experiential value, emphasizing a hedonic (intrinsic) and utilitarian (extrinsic) value component and the existence of the holistic concept of experiential value. Our results demonstrate that experiential value consists of the third order components hedonic (intrinsic) and utilitarian (extrinsic) value. Both value aspects impact attitudinal loyalty ultimately leading to behavioral loyalty which is also directly affected by utilitarian value. Finally, a nonhierarchical (k-means) cluster analysis identified four segments of online visitors: hedonists, utilitarians, active negativists, and reactive positivists.marketing ;

Customer perception of switch-feel in luxury sports utility vehicles

Successful new product introduction requires that product characteristics relate to the customer on functional, emotional, aesthetic and cultural levels. As a part of research into automotive human machine interfaces (HMI), this paper describes holistic customer research carried out to investigate how the haptics of switches in luxury sports utility vehicles (SUVs) are perceived by customers. The application of these techniques, including an initial proposal for objective specifications, is addressed within the broader new product introduction context, and benefits described. One-hundred and one customers of SUVs assessed the feel of automotive push switches, completing the tasks both in, and out of vehicles to investigate the effect of context. Using the semantic differential technique, hedonic testing, and content analysis of customers’ verbatim comments, a holistic picture has been built up of what influences the haptic experience. It was found that customers were able to partially discriminate differences in switch-feel, alongside considerations of visual appearance, image, and usability. Three factors named ‘Affective’, ‘Robustness and Precision’, and ‘Silkiness’ explained 61% of the variance in a principle components analysis. Correlations of the factors with acceptance scores were 0.505, 0.371, and 0.168, respectively

Verifying Dynamic Kano’s Model to Support New Product/Service Development

Purpose: Although firms try to shorten time-to-market, the duration of product development projects might anyway jeopardize the assumptions made at the beginning of the design process. This includes the definition of product attributes for ensuring customer satisfaction, thus forecasting techniques could be worthwhile. Within Kano’s method, trajectories of quality attributes have been identified and they can be potentially useful to the scope, but they have not been carefully verified. Design/methodology/approach: The paper takes on the above verification challenge by exploring studies of customer satisfaction conducted by means of Kano’s model regarding manifold industrial fields. The paper focuses on changes in the relevance of customer requirements reported in different contributions and analyses data statistically. Findings: The dynamic trajectories outlined in Kano’s model are partially confirmed and they are valuable in the mid-term to predict changes in customer preferences. The use of quantitative indicators portraying the extent of customer satisfaction and dissatisfaction leads to more reliable predictions. Research limitations/implications: In order to use as many data as possible, information has been gathered from different industrial fields, which can exhibit different paces in changes of customer preferences. Practical implications: The results benefit firms willing to have a clearer picture of customer main drivers for customer satisfaction at the time of market launch, although customer surveys are conducted at the beginning of product development projects. Originality/value: The paper puts into question previous assumptions about modifications of customer preferences, which, however are just empirically supported and assesses how these can be exploited in a reliable way.Peer Reviewe

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Gaussian process regression for virtual metrology of microchip quality and the resulting strategic sampling scheme

Manufacturing of integrated circuits involves many sequential processes, often ex- ecuted to nanoscale tolerances, and the yield depends on the often unmeasured quality of intermediate steps. In the high-throughput industry of fabricating microelectronics on semi-conducting wafers, scheduling measurements of product quality before the electrical test of the complete IC can be expensive. We therefore seek to predict metrics of product quality based on sensor readings describing the environment within the relevant tool during the processing of each wafer, or to apply the concept of virtual metrology (VM) to monitor these intermediate steps. We model the data using Gaussian process regression (GPR), adapted to simultaneously learn the nonlinear dynamics that govern the quality characteristic, as well as their operating space, expressed by a linear embedding of the sensor traces’ features. Such Bayesian models predict a distribution for the target metric, such as a critical dimension, so one may assess the model’s credibility through its predictive uncertainty. Assuming measurements of the quality characteristic of interest are budgeted, we seek to hasten convergence of the GPR model to a credible form through an active sampling scheme, whereby the predictive uncertainty informs which wafer’s quality to measure next. We evaluate this convergence when predicting and updating online, as if in a factory, using a large dataset for plasma-enhanced chemical vapor deposition (PECVD), with measured thicknesses for ~32,000 wafers. By approximately optimizing the information extracted from this seemingly repetitive data describing a tightly controlled process, GPR achieves ~10% greater accuracy on average than a baseline linear model based on partial least squares (PLS). In a derivative study, we seek to discern the degree of drift in the process over the several months the data spans. We express this drift by how unusual the relevant features, as embedded by the GPR model, appear as the in- puts compensate for degrading conditions. This method detects the onset of consistently unusual behavior that extends to a bimodal thickness fault, anticipating its flagging by as much as two days.Mechanical Engineerin

INTEGRATING KANO MODEL WITH DATA MINING TECHNIQUES TO ENHANCE CUSTOMER SATISFACTION

The business world is becoming more competitive from time to time; therefore, businesses are forced to improve their strategies in every single aspect. So, determining the elements that contribute to the clients\u27 contentment is one of the critical needs of businesses to develop successful products in the market. The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model focuses on highlighting the most relevant attributes of a product or service along with customers’ estimation of how these attributes can be used to predict satisfaction with specific services or products. This research aims at developing a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to improve the quality of United Arab Emirates University academic support and development services provided to their students by solving the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from United Arab Emirates University. The experiment suggests that Extreme Gradient Boosting Regression can produce the best results for this kind of problem. Based on the integration of the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that either Chi-Square or Analysis of Variance (ANOVA) features selection model’s integration with the Kano model giving higher values of Pearson correlation coefficient and R2. Moreover, the prediction was made using union features between the Kano model\u27s most important features and the most frequent features among 8 clusters. It shows high-performance results

Assessment of natural groundwater recharge in Tudun Wada Local Government Kano State, Nigeria

Water is important natural resources on earth which cater for all human endeavors. Estimation of groundwater recharge is an essential for efficient groundwater resources management. This research entails the assessment of natural groundwater recharge in Tudun Wada Kano, Nigeria. This study carried out on 2015 attempted to derive an empirical relationship to determine the groundwater recharge from rainfall in Tudun Wada based on seasonal groundwater balance using data from 2002 to 2013. This empirical relationship similar to Chaturvedi formula was derived by fitting the estimated values of rainfall recharge and the corresponding values of rainfall in the monsoon season through the non-linear regression techniques. The variance was found to be 29.50 %, and the recharge of groundwater commences at P = 15.28 inches and the relative errors was found to range from 0.95 to 28.43%.Keywords: groundwater; rainfall; recharge; wet season; water balance; empirical relatio

The Kano Model: How the Pandemic Influences Customer Satisfaction with Digital Wallet Services in Indonesia

Introduction/Main Objectives: This study aims to evaluate the effectiveness of digital wallets service qualities using Kano Model. Understanding customer needs and satisfaction is crucial in developing products. Background Problems: In the “new normal” era, payment method is predicted to shift to digital wallets. For quality improvement, digital wallet companies should understand customer needs and satisfaction. This study used the Kano model to analyze customer needs and satisfaction in OVO and ShopeePay, two digital wallets widely used for online shopping transactions during COVID19. Novelty: Although studies into the Kano model implementation towards e-commerce exist, there are no specific studies on the model implementation concerning digital wallet payments amid COVID-19. The Kano model is significant in understanding which software products generate high customer satisfaction, which will give a greater influence, as well as necessary attributes for the customers. Research Methods: This study employed the Kano model as one of the most relevant method to measure customer satisfaction by measured of each attribute’s quality. Finding/Results: Most features of OVO and ShopeePay are categorized into the “must be” and “one dimensional” category. The satisfaction map results indicate that most items placed in the “indifferent” quadrant denote the unfulfilled customer expectations. Conclusion: OVO and ShopeePay should improve the items placed in the “indifferent” quadrant first. This study contributes empirically and theoretically by emphasizing the Kano model’s utility for digital wallet services and providing new insights for digital wallet companies and the government on Indonesians’ usage of digital wallets in the “new normal”.