Customer Approach Research Articles

The transformative and evolutionary approach of omnichannel in retail companies: insights from multi-case studies in Portugal

PurposeOmnichannel implementation in retail requires business transformation and faces several operational barriers. This research discusses how omnichannel has been implemented, in a managerial perspective, and how integration of operations has overcome the identified transformative barriers.Design/methodology/approachThe authors followed a multi-case study approach, with observation and interviews with managers of four big retail companies in Portugal.FindingsThe results suggest that retail companies have overcome the barriers to implement omnichannel models through the integration of information technology (IT), the accomplishment of organisational changes and the optimisation of customer feedback, achieving positive business indicators, namely increased sales.Research limitations/implicationsThe conclusions of the paper provide valuable information to help companies to design the process of channel's integration in order to overcome the transformative constraints of omnichannel. However, those conclusions emerge from Portuguese case studies of retail companies, and a generalised discussion should consider the contextual diversity of consumer expectations, cultural user experiences in retail and the maturity of digital transformation and omnichannel implementation stage in each country.Originality/valuePrevious studies had characterised the items of omnichannel retail, fulfilment processes, the benefits of channels' integration in customer experience, satisfaction and loyalty and had identified barriers for its implementation. Considering the existence of different stages of omnichannel implementation, this paper explores how retail companies increment omnichannel operations, overcome transformative barriers and achieve the omnichannel benefits, through the whole involvement of the organisation system, the customer approach and the business model, besides the technology integration.

Customization of e-textile sensory tools for people with dementia

Sensory activities are used to increase social engagement for people with dementia, who may struggle to participate in activities due to deteriorating cognitive functions. Sensory tools are used but these can be inappropriate since they are not designed for people with dementia. This paper details the design of an e-textile sensory tool for people with dementia, with stakeholders in Mainland China. The concept, a sensory wall, was derived from a previous co-design project in Hong Kong. However, the design context differs, and this work highlights barriers to implementing co-design in sensory tool design. Instead, this project took a collaborative customization approach. This paper describes the design processes and evaluations for the first and second sensory wall, with context on dementia care in Mainland China. This paper highlights considerations in the design of a sensory tool for people with dementia, and the benefits of stakeholder engagement in sensory tool design.

DESIGN OF SALES PERFORMANCE DASHBOARD BASED ON SALES FUNNEL & SALES FORCE AUTOMATION THEORIES: A CASE OF AN INDONESIAN ISLAMIC BANK

One of the bank management concerns related to sales performance is ensuring sales people perform the sales process correctly. Unfortunately, most sales people have problems in the sales process, which causes their low performance. Islamic banks in Indonesia also experienced this situation, so a strategy needs to overcome this problem. For instance, using surveillance tools such as Sales Force Automation (SFA) with sales funnel theory. An SFA tool is a sales system connected to computers and internet networks. It monitors sales processes and results, manages contacts, forecasts sales, and analyzes sales performance daily and monthly. Additionally, it helps sales forces to measure the ability to achieve targets, plan sales strategies and customer approaches, and increase work motivation. Also, raise awareness of developing skills, and provide up-to-date information to supervisors about sales force performance to determine the proper training. Therefore, the use of SFA tools is expected to increase the productivity of sales people in optimizing the fulfillment of deposit targets in Islamic banking. This paper reports the SFA design in the form of a dashboard model with a visual sales to funnel that shows the function of the salesperson in conducting the sales process ends with the results.

Knowledge Gaps Update to the 2019 IPCC Special Report on the Ocean and Cryosphere: Prospects to Refine Coastal Flood Hazard Assessments and Adaptation Strategies With At-Risk Communities of Alaska

This article reviews the status of knowledge gaps and co-production process challenges that impede coastal flood hazard resilience planning in communities of northwestern Alaska, where threat levels are high. Discussion focuses on the state of knowledge arising after preparation of the 2019 IPCC Special Report on the Ocean and Cryosphere in a Changing Climate and highlights prospects to address urgent needs. The intent is to identify some key steps necessary to advance the integration of relevant multidisciplinary observations with flood modeling and infrastructure mapping to co-produce new online hazard and risk assessment tools that inform local community planning and improve science collaboration among Federal, state, and regional partners for enhanced pre-storm preparations and post-storm recovery, including partial or complete relocation. By focusing coastal data integration for delivery of priority geospatial hazard map products through a consistent yet customized approach to adaptation planning, the broad collaborative effort in Alaska may yield a path of stakeholder service delivery that can be applied to many Arctic communities and other vulnerable regions of the world.

CPU runtime optimization in data damage tracking quarantine and recovery (DTQR) scheme based on customized ANN

Database Management Systems (DBMS) are regularly used to store and process touchy endeavour information. In any case, it is beyond the realm of imagination to expect to verify the information by depending on the entrance control and security instruments of such frameworks alone; clients may handle their benefits or go around security systems to malevolently adjust and get to the information. Hence, we have developed a reliable, secure, and real-time data damage tracking Quarantine and recovery scheme using Customized ANN approach. The proposed DTQR scheme recovers the accurate data from any newer data and eliminates the fraudulent data. The approach also provides a solution for runtime problems occurring in the DBMS. Moreover, the proposed technique implemented in the working platform of JAVA and the results are analyzed with existing techniques to prove the efficiency of the proposed system.

Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer

Travel and hospitality industry are adopting high end technology to reach out their customers. New levels of disruption are being introduced using Artificial Intelligence and Machine Learning techniques of research. In the realm of physical and tangible aspects of service, since this sector have vast amounts of data available application of these techniques are being tested to explore various aspect of the customer experience. Data analysis algorithms like machine learning have been used by firms across the world to analyze these data and understand various facets of customer experience. However, these algorithms suffer from blackbox issues where businesses do not know how the outcome was achieved and if the analysis is biased in some way. In this study, we utilize an explainable machine learning approach on Airbnb data to understand solve two challenges associated with large-scale marketing data. First, we identify and prioritize the customer experience factors like bedrooms, host status, host response rate etc. that have an impact on product pricing. Secondly, we build and test an explainable predictive model on the same. Our research has significant implications for academia and industry in identifying applications of explainable algorithms in decision-making.

Application of digital twins in specific manufacturing processes

Following the requirements of the modern economic environment is a challenge for entrepreneurs. The more, that there is an increasing need to manufacture product for individual customers, the so-called customization approach. As a result, the production is individualized, so that the repeatability of products and stocks is low. This forces companies to apply specific organizational development strategies such as Lean and Agile. It also forces an increase in flexibility of the implementation of individual production tasks of enterprises. Increased production flexibility, at the same time, influences a greater number of decisions made, data generation and the use of enterprise resources. For this purpose, entrepreneurs use various simulations approaches, including applications for simulation of complex production processes, but nowadays they are increasingly leaning towards the so-called digital twins. The article presents the possibility of using a digital twin and the necessary data required to make such a twin in order to reflect the actual activities carried out in production.

Influence of Key Performance Indicators in Marketing on the Financial Situation of Wine Producers Using ICT

Marketing is one of the key elements of the success of all companies, including the wine sector. Given the importance of wine producers for agriculture, it is important to define and monitor key performance indicators in marketing (KPIs) for a successful stay in the market and a competitive position at home and abroad. Today, the increase in competitive advantage includes mainly marketing, innovation and information and communication technologies. New digital tools and innovations have changed the way we approached data and decisions. A modernly adapted and effective strategic marketing strategy represents for wine companies an understanding mainly of their possibilities as well as the possibilities to influence the customer. This article evaluates the key performance indicators in marketing (KPI) and its relationship and impact on the financial situation of wine producers in Slovakia. The research sample includes 80 respondents. We obtained the primary data through a questionnaire, which was filled in by the leaders of wine companies. We verified the accuracy by means of descriptive statistics and multiple linear regression and Kruskall-Wallis test. We have verified the reliability of the data with the Cronbach alpha test. We have formulated scientific assumptions for in-depth analysis: hypothesis 1 – assumes that key performance indicators have a significant influence on financial situation of selected companies, hypothesis 2 – the use of ICT in marketing is statistically related to the key performance indicators. The results showed a statistically significant impact of KPIs on the financial situation of companies. We have identified significance in customer satisfaction and loyalty, brand awareness and return of investment. However, we were unable to statistically confirm the impact of other indicators (sales growth, market share, gaining new customers). We also identified significant differences in the use of ICT in marketing with key performance indicators in hiring new customers and return on investment. This research contributes positively to the importance of brand building in the eyes of customers as well as customer service, building loyalty and satisfaction, which returns to the loyal approach of customers to the repurchase of wine products and provides advice for professionals. Return on investment helps in more accurate business decisions that can be used when purchasing new equipment (technology), hiring employees, or properly assessing the profitability of marketing strategies.

Revealing travellers’ satisfaction during COVID-19 outbreak: Moderating role of service quality

User-Generated-Content (UGC) has gained increasing attention as an important indicator of business success in the tourism and hospitality sectors. Previous literature has analyzed travelers' satisfaction through quantitative approaches using questionnaire surveys. Another direction of research has explored the dimensions of satisfaction based on online customers' reviews using the machine learning approach. This study aims to present a new method that combines machine learning and survey-based approaches for customers' satisfaction analysis during the COVID-19 outbreak. In addition, we investigate the moderating role of service quality on the relationship between hotels' performance criteria and customers' satisfaction. To achieve this, the Latent Dirichlet Allocation (LDA) was used for textual data analysis, k-means was used for data segmentation, dimensionality reduction approach was used for the imputation of the missing values, and fuzzy rule-based was used for the prediction of satisfaction level. Following that, a survey-based approach was used to validate the research model by distributing the questionnaire and analyzing the collected data using the Structural Equation Modeling technique. The result of this research presents important contributions from the methodological and practical perspectives in the context of customers' satisfaction in tourism and hospitality during the COVID-19 outbreak. The outcomes of this research confirm the significant influence of the quality of services during the COVID-19 crisis on the relationship between hotel services and travellers’ satisfaction.

Сучасні рекомендації щодо лікування псоріатичного артриту згідно з рекомендаціями Американського коледжу ревматологів і Національної організації з псоріазу 2018 року

Псоріатичний артрит (ПсА) — це хронічне запальне скелетно-м’язове захворювання, асоційоване з псоріазом, яке маніфестує зазвичай з периферичного артриту, дактиліту, ентезиту та/чи спондиліту. Рання ідентифікація та діагностика ПсА, ранній початок терапії важливі для покращення довгострокових наслідків хвороби. Клінічна картина ПсА є гетерогенною, і лікарі досить часто стикаються з проблемами при визначенні стратегій лікування. Метою нашого огляду були надання й інтерпретація рекомендацій Американського коледжу ревматологів (ACR) і Національної організації з псоріазу (NFP) 2018 року з лікування активного ПсА у дорослих з використанням фармакологічних і нефармакологічних методів. Ці рекомендації щодо лікування ПсА можуть допомогти як клініцистам, так і пацієнтам досягти оптимальних рішень щодо менеджменту хвороби. У рекомендаціях наведені стратегії вибору препаратів для лікування активного ПсА при різних клінічних ситуаціях та залежно від проведеної попередньої терапії. Наявність або відсутність коморбідних станів, таких як запальне захворювання кишечника, увеїт, діабет або серйозні інфекції, а також знання попередніх методів лікування є факторами, що дозволяють зробити вибір оптимальної терапії для окремого пацієнта в даний момент часу, застосовуючи індивідуальний підхід. Рекомендації ACR/NFP щодо лікування активного ПсА рекомендують інгібітори фактора некрозу пухлини, а не пероральні низькомолекулярні препарати як препарати першої лінії, саме ці біологічні агенти запобігають прогресуванню захворювання та пошкодженню суглобів. Наголошується на необхідності ранньої та агресивної терапії у пацієнтів з вперше виявленим ПсА.

A black-box adversarial attack for poisoning clustering

Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests and neural networks.

Дизайн-мышление и креативные технологии в управлении проектами: многостадийный подход

The present article is devoted to the issues of applying design thinking and other creative technologies destined at the development of innovational solutions of the existing tasks and restrictions in project management processes. The authors are developing a conceptual framework by defining the focus of application of creative technologies in the project management practice as well as are proposing a custom approach to the application of the relevant instruments based on the project type and its lifecycle phases. The findings are made on the basis of fundamentals of project management, strategic management and design thinking methodology, as well as the authors’ own applied studies and experience in project and program management. The proposed approach allows broadening theoretical learnings on the project lifecycle stages from the perspective of creating and developing business innovations, as well as on the project manager role and skills, which in turn helps to resolve complex issues and controversies connected to the application of the existing project management methods in the organizational development and innovation product creation processes.

Comprior: facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets

BackgroundReproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness.ResultsWe present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance.ConclusionComprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness.

METHODOLOGY OF AUTOMATED SELECTION OF FOOTWEAR IN THE TASKS OF SUPPORTING DECISION MAKING IN ORTHOPEDIC PROVIDING OF PATIENTS

Nowadays, customization is becoming an increasingly applicable approach for the individualization of goods made in mass production. Another trend is the technology of virtual, or contactless, shoe fitting, aimed at selecting goods in accordance with the individual anthropometric parameters of customers' feet. The article is considered to the presentation of the results of scientific developments in the field of optimization of orthopedic providing of patients using customization approaches and conceptual solutions of contactless shoe fitting technologies. As part of the study, an algorithm of shoe selection and a method for assessment of matching the internal shape of the shoe to the patient’s feet measurements have been developed. The possibilities and conditions for the application of the results obtained in the tasks of supporting orthopedic specialists in the orthopedic providing of patients on the basis of data mining in the implementation of the automated remote selection of shoes and ordering for custom shoes are presented. The reported study was funded by RFBR, project number 19-37-90152.

A Mixture of Experts Approach for Low-Cost DNN Customization

Editor's notes: This article introduces a novel architecture for local learning, namely mixture of experts (MoEs), to customize a deep neural network (DNN) that is deployed on an edge device with performance improvement and low implementation overhead. -Deming Chen, University of Illinois at Urbana-Champaign

Quality Driven Approach for Product Line Architecture Customization in Patient Navigation Program Software Product Line

Quality Driven Approach for Product Line Architecture Customization in Patient Navigation Program Software Product Line

Classifying and Predicting Surgical Complications After Laryngectomy: A Novel Approach to Diagnosing and Treating Patients.

The total laryngectomy is one of the most standardized major surgical procedures in otolaryngology. Several studies have proposed the Clavien-Dindo classification (CDC) as a solution to classifying postoperative complications into 5 grades from less severe to severe. Yet more data on classifying larger patient populations undergoing major otolaryngologic surgery according to the CDC are needed. Predicting postoperative complications in clinical practice is often subject to generalized clinical scoring systems with uncertain predictive abilities for otolaryngologic surgery. Machine learning offers methods to predict postoperative complications based on data obtained prior to surgery. We included all patients (N = 148) who underwent a total laryngectomy after diagnosis of squamous cell carcinoma at our institution. A univariate and multivariate logistic regression analysis of multiple complex risk factors was performed, and patients were grouped into severe postoperative complications (CDC ≥ 4) and less severe complications. Four different commonly used machine learning algorithms were trained on the dataset. The best model was selected to predict postoperative complications on the complete dataset. Univariate analysis showed that the most significant predictors for postoperative complications were the Charlson Comorbidity Index (CCI) and whether reconstruction was performed intraoperatively. A multivariate analysis showed that the CCI and reconstruction remained significant. The commonly used AdaBoost algorithm achieved the highest area under the curve with 0.77 with high positive and negative predictive values in subsequent analysis. This study shows that postoperative complications can be classified according to the CDC with the CCI being a useful screening tool to predict patients at risk for postoperative complications. We provide evidence that could help identify single patients at risk for complications and customize treatment accordingly which could finally lead to a custom approach for every patient. We also suggest that there is no increase in complications with patients of higher age.

Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills.

Mobility has been one of the most impacted aspects of human life due to the spread of the COVID-19 pandemic. Home confinement, the lack of access to physical rehabilitation, and prolonged immobilization of COVID-19-positive patients within hospitals are three major factors that affected the mobility of the general population world-wide. Balance is one key indicator to monitor the possible movement disorders that may arise both during the COVID-19 pandemic and in the coming future post-COVID-19. A systematic quantification of the balance performance in the general population is essential for preventing the appearance and progression of certain diseases (e.g., cardiovascular, neurodegenerative, and musculoskeletal), as well as for assessing the therapeutic outcomes of prescribed physical exercises for elderly and pathological patients. Current research on clinical exercises and associated outcome measures of balance is still far from reaching a consensus on a “golden standard” practice. Moreover, patients are often reluctant or unable to follow prescribed exercises, because of overcrowded facilities, lack of reliable and safe transportation, or stay-at-home orders due to the current pandemic. A novel balance assessment methodology, in combination with a home-care technology, can overcome these limitations. This paper presents a computational framework for the in-home quantitative assessment of balance control skills. Novel outcome measures of balance performance are implemented in the design of rehabilitation exercises with customized and quantifiable training goals. Using this framework in conjunction with a portable technology, physicians can treat and diagnose patients remotely, with reduced time and costs and a highly customized approach. The methodology proposed in this research can support the development of innovative technologies for smart and connected home-care solutions for physical therapy rehabilitation.

THE IMPACT OF THE ECONOMIC CRISIS ON THE FINANCIAL SITUATION OF ENTERPRISES OF THE QUALITY OUTSOURCING SECTOR

The article describes the impact of the economic crisis caused by the global COVID-19 pandemic on enterprises from the service outsourcing industry operating in 11 European countries. The following research objectives have been indicated: 1) learning the opinions of managers on the crisis and its impact on the company, 2) indicating positive and negative features of the economic situation related to the global pandemic, 3) the analysis of the financial situation during the pandemic. The methodology adopted in the research is based on the descriptive approach, using the case study analysis. For the purposes of the research, the structured interview questionnaire was used. The questions asked concerned the description of the specificity of the business, the company’s location, the impact of the economic crisis on the situation of enterprises, the description of the positive and negative impact of the crisis on the operation of enterprises. The area of interest covered the subjective opinions of managers on the existing economic situation, which is metaphorically compared to the black swan in the article. The research conducted in the companies from 11 European countries indicated that in the period of March-August 2020, due to the strong dependence of the sector on the automotive industry, the companies were affected by the crisis. The turnover and the number of employees decreased dramatically. The slow recovery of contractors from the crisis as well as the change in customer approach to staff employment have changed the operating conditions of the quality outsourcing sector companies. Flexible adaptation to customer requirements has brought about that all the analyzed companies recorded an increase in the sales of their services, counting on the further good prosperity of the industry. © 2021, Czestochowa University of Technology. All rights reserved.