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Insurance is a crucial mechanism used to lighten the financial burden as it provides protection against financial losses resulting from unexpected events. Insurers adopt various approaches, such as machine learning, to attract the uninsured. By using machine learning, a company is able to tap into the wealth of information of its potential customers. The main objective of this study is to apply artificial neural networks (ANNs) to predict the propensity of consumers to purchase an insurance policy by using the dataset from the Computational Intelligence and Learning (CoIL) Challenge 2000. In addition, this study also aims to identify factors that affect the propensity of customers to purchase insurance policies via feature selection. The dataset is pre-processed with feature construction and three feature selection methods, which are the neighbourhood component analysis (NCA), sequential forward selection (SFS) and sequential backward selection (SBS). Sampling techniques are carried out to address the issue of imbalanced class distributions. The results obtained are found to be comparable with the top few entries of the CoIL Challenge 2000, which shows the efficiency of the proposed model in predicting consumers’ intention of purchasing insurance policies.

This editorial aims to summarize the special issue entitled “Sustainable Solutions for Internet of Things Using Artificial Intelligence and Blockchain in Future Networks”, which deals with the impacts of recent infrastructure development using the Internet of things. This special issue consists of four scientific articles.

This study explores the factors of e-commerce adoption among Sarawakians. One of the factors is the level of cybersecurity awareness. We aim to assess the readiness for e-commerce among Sarawakians due to the lack of study conducted on the subject. A research model based on the perceived risk (PR), perceived usefulness (PU) and perceived quality of products (PQ), and the intention (I) of adoption of e-commerce services in Sarawak is proposed. The validity of the proposed model is then tested using various validity tests such as item reliability, construct validity, convergent validity and discriminant validity via the SmartPLS software. Once the validity of the model has been determined, a structural equation model is used to study the strength of the model before the test of the hypothesis can be done. The effect size, f2, is calculated by using SmartPLS. The index value of each variable is also plotted in the importance-performance matrix analysis (IPMA). Based on the survey data from 128 end users in Sarawak, the study finds that PU is the most crucial factor in adopting e-commerce services, followed by PQ. Surprisingly, PR does not play any role in the intention of Sarawakians to adopt e-commerce services. The results suggest several important key points as follows: (i) the Sarawak Government and its e-commerce partners should focus on educating the people of Sarawak on the importance of cybersecurity to prevent cyber-related crimes from occurring and causing massive damage in Sarawak’s attempt to digitise its economy; (ii)Sarawakians prefer functional e-commerce services; (iii) the quality of e-commerce products should also be maintained; and (iv) the developers should focus on the usefulness of their products to ensure that their service can be adopted by the people of Sarawak.

The surge in interest in online teaching increased not only due to the pandemic. It had been growing even before. The main objective of this study is therefore to explore how online teaching has changed. It addresses experience and opinions of educators of Czech and Slovak universities in the period from the first days of the COVID-19 lockdown (March 2020) till the peak of its second wave (May 2021). To examine the impact of disharmony, the authors investigated Czech and Slovak university educators’ activities and behaviour during their online teaching. A descriptive statistics approach was applied. A total of 172 educators participated in our online survey. Our results reveal that online teaching has become a fundamental component of their education. Our outcomes demonstrate their low preparation for this unexpected event as well as their quick adaptation to the new situation. Additionally, data indicate that their difficulties reconcile their previous experience and teaching practices with online teaching. Finally, they show that about half of them are still sceptical about the future of online education and dream of return to traditional teaching. Our results also indicate that universities should facilitate their efforts in developing online education methodology and overall support to their educators.

E-commerce has become very important in our daily lives. Many business transactions are made easier on this platform. Sellers and consumers are the two main parties that gain a lot of benefits from it. Although many sellers are attracted to set up their businesses on this online platform, it also causes challenges such as a highly competitive business environment and unpredictable sales. Thus, we propose a data analytics approach for short-term sales forecasts using limited information in the e-commerce marketplace. Product details are scraped from the e-commerce marketplace using a content scraping tool. Since the information in the e-commerce marketplace is limited and essential, scraped product details are pre-processed and constructed into meaningful data. These data are used in the computation of the forecasting methods. Three types of quantitative forecasting methods are computed and compared. These are simple moving average, dynamic linear regression and exponential smoothing. Three different evaluation metrics, namely mean absolute deviation, mean absolute percentage error and mean squared error, are used for the performance evaluation in order to determine the most suitable forecasting method. In our experiment, we found that the simple moving average has the best forecasting accuracy among other forecasting methods. Therefore, the application of the simple moving average forecasting method is suitable and can be used in the e-commerce marketplace for sales forecasting.

In this article, we describe the use of dynamic simulation when designing an effective system for order picking within a distribution warehouse. The simulation model was created in the Witness software environment for discrete dynamic simulation and is a modification of a general simulation model of material flows in supplier systems. Using the example of a batch system for picking orders in a drugstore goods warehouse, we discuss the possibilities of using a general simulation model of material flows as an effective framework for the development of system support for warehouse processes using WMS. The simulation model is based on the possibility of dividing any material flow in the supply system into a finite number of movements with the possibility of using one of the sources and fulfilment of certain conditions. In order to achieve the required optimisation of the order picking system, which depends, in particular, on the unknown duration of goods collection at the picking location, and on the duration of goods sorting in consolidation, the “what-if” analysis has been used as a tool to measure the impact of uncertainty of one or more variables entering the model on the uncertainty of output variables. The study showed that minimisation of the number of physical elements in the model leads to a significantly higher speed of its operation. By means of dynamic simulation, it is possible to test a large number of variants of the picking system layout in a relatively short time and minimise the risk of erroneous decisions associated with the implementation of a suitable WMS.

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.

The text is devoted to a consideration of the “information explosion” phenomenon. The exponential growth of publications is compared to the (similarly exponential) growth of population, especially in the countries where most of the publications are created. The increasing tertiary education gross enrolment ratio (naturally associated with involvement in the publication process) is also taken into account. The text comes to a conclusion that either the exponential growth of publications must decrease its base value in our future, or we are heading towards a time point where an increasing number of publications find no readers (if that point is not yet behind us).

The article briefly presents the main results of an applied research project to the professional public. The project output is a solution that enables the recognition of selected types of traffic signs using artificial intelligence for image recognition. This computationally intensive process is implemented in mobile phones. In order to achieve the involvement of the general public in the collection of data on transport infrastructure, the entire solution is part of navigation for mobile phones and supported by two functions that motivate users to collect data, i.e., scan the area in front of the vehicle with the phone's camera. The first function is the projection of the route into the real environment (the so-called augmented reality mode), and the second function is the possibility of video recording the drive. The video recording is cryptographically signed to ensure authenticity in administrative or judicial proceedings, e.g., when proving the course and circumstances of a traffic accident. The collection of data on transport infrastructure is completely anonymous in compliance with applicable laws. The data about recognized traffic signs will not only serve the navigation provider to improve the user experience but the processed data will also be exported to community-created world maps (project OpenStreetMap).

Today information systems are an inseparable part of all companies. This study deals with the non-financial implications occurring while implementing the enterprise resource planning software called AROP in 3 manufacturing companies being in different implementation phases. The work was based on semi-structured interviews, which were supplemented by a questionnaire survey with the management and the employees of the companies. The primary goal was to determine both the expected and the real impacts associated with the introduction of AROP software in the companies. The secondary goal was to determine satisfaction with AROP training in individual companies and to propose countermeasures. It was found that the company using AROP software in the full version confirmed a number of impacts that the companies had expected in the testing phase and in the phase just before the start of the testing phase. Examples of the confirmed impacts are following: merging all information into one system, obtaining previously unknown information or simplifying the work of employees. During the training, some weaknesses were identified, such as the length of the training or the form of the training. Therefore, several recommendations were proposed in order to streamline the implementation of AROP software and improve the training of this system.

The goal of this case study is to compare websites from 9 different countries―Austria, Chile, China, Japan, Latvia, Nigeria, Saudi Arabia, the US and the Czech Republic―and, based on this comparison, to provide the missing linking of website elements with cultural dimensions for better cultural adaptation of web content. Hofstede’s cultural dimensions were used for the selection of countries for this study. To examine the influence of culture on websites, countries with extreme values of cultural dimensions were selected. An important benefit is that this study takes into account all of Hofstede's cultural dimensions, including the latest one (indulgence vs restraint). For each country 50 websites were selected from areas that most closely reflect the culture of the country. The main focus was on the selection of an appropriate representative sample of websites for each state. A total of 450 pages was analyzed. For each website, 42 web elements determined as the most important were monitored. Moreover, the presence of various types of social networks and five general characteristics were monitored. The findings show that culture influences website design. The results of this study reveal a connection between website elements and Hofstede’s cultural dimensions. For example, headlines are important for countries with a high value of individualism, uncertainty avoidance, and a low value of power distance and indulgence. Newsletters are associated with a high value of indulgence and a low value of long-term orientation and search option with a high value of power distance. Overall, about 20 culturally sensitive website elements were identified. The study also provides a comprehensive overview of website characteristics for each of the selected countries. For UX designers, web localization specialists, academicians, and web developers, this study provides an original view into culturally sensitive website elements and features.

Big data analytics refers to a set of advanced technologies, which are designed to efficiently operate and maintain data that are not only big, but also high in variety and velocity. This paper analyses these emerging big data technologies and presents a comparison of the selected big data analytics platforms through the whole data life. The main aim is then to propose and demonstrate the use of an AHP model for the big data analytics platform selection, which may be used by businesses, public sector institutions as well as citizens to solve multiple criteria decision-making problems. It would help them to discover patterns, relationships and useful information in their big data, make sense of them and to take responsive action.

The main objective of this special issue is to bring together diverse, novel and impactful research work on explainable deep learning for medicine based on the Internet of Medical Things, thereby accelerating research in this field. The regulation of Internet of Medical Things (IoMT) aware industrial networks for medical science applications has been evolving day by day. An IoMT industrial network consists of different bio-medical sensors, wireless technologies and cloud computing services to run different healthcare applications. However, IoMT industrial networks also suffer from dynamics uncertainties, such as intermittent changes in wireless network values, availability of cloud services and security issues, and require flexible systems to cope with these challenges for healthcare applications in the network.

The editorial summarizes the development of the Acta Informatica Pragensia journal over the last four years (2019–2022). Thanks to the indexing of the journal in the Scopus citation database since 2019, the journal has not only consolidated its position in Central Europe but also began to build its position in the international community of informaticians. The paper presents Scopus metrics and statistics of submitted and accepted articles for the observed period of four years. Furthermore, it presents Acta Informatica Pragensia in the context of other informatics-oriented journals published in the Czech Republic and indexed in the Scopus or Web of Science citation databases. An overview of the main changes over the given period and plans for the further development of the journal are presented at the end of the paper.

Clinical decision support systems are computer systems that help decision-makers make effective and efficient decisions in the diagnosis and treatment of diseases, patient care, and health institution management. In this study, descriptive and bibliometric analyses were carried out of studies on clinical decision support systems conducted between 2016 and 2021 in journals included in the Web of Science database. In the first stage, the distribution of the articles was determined according to the publication year, country, institution, journal and citation numbers within the scope of descriptive analysis. In the second stage, within the scope of bibliometric analysis, co-author-institution, co-citation-author, co-citation-country and common keyword analysis of the articles were made. As a result of the research, 8052 articles on clinical decision support systems were reached. It was observed that the country with the most studies on this subject are the United States and the institution is Harvard University. It was seen that the journal with the highest number of articles was PLoS ONE. In addition, co-citations, institutional and country collaborations between authors are presented with visual maps. The purpose of this study is to present the studies on clinical decision support systems in a systematic framework and to provide a broad perspective to the researchers and field experts working on this subject.

The goal of this paper is to examine whether, in Q-system inventory control policy, a combination of the reorder point exceeding order quantity leads to minimal holding and ordering costs when dealing with sporadic demand. For this purpose, a past stock movement simulation is applied to a set of randomly generated data with different numbers of zero demand periods ranging from 10 to 90%. The outputs of the simulation prove that in situations where stock holding costs are too high, the simulation tends to reduce average stock by overcoming periods between two demand peaks with an increase in the numbers of small replenishment orders and reaches lower stock holding and ordering costs. Furthermore, the correlation analysis proves that there is a statistically significant relationship (r = .847, p = .004) between the number of time series that reach minimal holding and ordering costs under the control of reorder point (replenishment order) and the demand standard deviation affected by the evolving sporadicity. These findings can support decision making linked with inventory management of products with sporadic demand and contribute to development of business information systems.

Mental health is an important issue for university students. The objective of this article was to apply and compare the different classification methods for students’ mental health problems. Furthermore, it presents an ensemble classification method to improve the accuracy of classifiers and assist psychologists in the decision making process. For this, 10 different classifiers were applied for classifying students into two groups. In addition, two methods of combining the classifiers are presented. In the first proposed method, the classifiers were selected based on their accuracy, and then voting was carried out based on maximum probability. In the second proposed method, the methods were combined based on the fields of the confusion table, and the voting was carried out based on majority voting scheme. These two methods were evaluated in two ways. Focusing on the accuracy and the maximum probability voting, the accuracy of the first method was 92.24%, whereas in the second method, it was 95.97%. Further, using confusion table and majority voting applied to the entire dataset, the accuracy reached 96.66%. The results are promising to assist the process of mental health assessment of students.

Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.

This paper deals with Greiner’s model, which is considered as an alternative concerning the life cycle of companies. There are many models that describe the life development of enterprises. The models differ in the number of individual phases, the size of the undertakings or their use. This paper aims to verify Greiner’s model in the Czech Republic. The research was carried out in 2019 from September till the end of November and within the geographical delimitation of the Liberec region. The research included entrepreneurial subjects from the field of national economy – automotive industry. A characteristic of the companies in question is the effort to increase automation and robotization using various IT programs with the support of their IT department or external suppliers. In terms of their size, these were enterprises with a large number of employees (over 250). Due to the necessity of capturing their historical development, only enterprises established in the year 1999 or earlier were included in the research. The total database of surveyed enterprises contains 14 subjects, of which six enterprises participated in the research. The research was divided into two main parts. The first was a questionnaire survey carried out through an electronic link. This was followed by a controlled interview with selected representatives of the cooperating companies. The research has verified the assumption that the companies involved are at least in the third phase, i.e. in the delegation phase and above in Greiner’s model. This was true about five out of the six companies. A benefit for the selected companies in relation to the identification of a specific phase of Greiner’s model can be seen in the fact that the companies can anticipate the coming crisis, prepare for it and take appropriate measures. This enables to resolve the crisis relatively quickly and the company can move to the next stage – stage of evolution. The second assumption confirmed in four of the 6 subjects in the survey is that business units, regardless of the stage of Greiner’s model, are targeting the three main features of the previous stages of revolution / evolution. These are the following: profit-orientation, high work commitment of company employees and tasks and increasingly specialized assignments.

Since the massive expansion of the Internet into a commercial world, the security of computer systems has become a priority. There are other areas that see an increase in the inclusion of the Internet, like national governments, hospitals, and university systems. All these systems contain highly sensitive information. In an effort to increase the security of internal data, we propose a novel method for the detection of automated computer attacks. This method was tested on a custom dataset prepared from the logs of the university information system at Prague University of Economics and Business. Two datasets were used. The first dataset contained only simple attacks, while the second one comprised the advanced attacks. The compiled and anonymized datasets were uploaded to BigML framework, where K-means, Isolation Forest and Logistic Regression algorithms were used in order to validate the proposed novel method. Our results showed that the proposed method is viable in cases where the attack volume is high and the time spacing between the actions is similar, which was verified on both tested datasets. It reached the detection rate of 93.57% in the case of simple attacks dataset, and 95.37% in the case of advanced attacks dataset. It reached similar detection rates as other algorithms used in the commercial environment. Based on this project, the proposed method can be implemented into the university information system in order to prevent these types of attacks in the future.

Digital transformation requires usage of effective software development methods, techniques, and tools for innovative IT solutions development. Agile methods, that are broadly adopted in recent years, fulfil this role. The aim of this report is to point to Agile surveys conducted in the Czech Republic. First survey was focused on the state of agile method adoption in companies in the Czech Republic and was conducted in 2019. Second survey, conducted in 2020, was focused on education of agile approaches at universities in the Czech Republic. The report shows, there is a contradiction between practise needs and the status of university education in the Czech Republic which is compared to the status in the world.

As a professional virtual community, the virtual academic community meets the new needs of scholars for academic cooperation in the network environment. It provides a more convenient way for scientific research cooperation. The purpose of this paper is to combine the factors influencing researcher cooperation in virtual academic communities and to verify and improve the index system of the factors influencing researcher cooperation in virtual academic communities with data support. Data for the research was obtained in an online questionnaire survey wjx.cn from forum muchong.com, which is the largest virtual academic community in China. Using principal component analysis method provides an in-depth data analysis of individual factors. The SPSS 20 was used to conduct a preliminary descriptive statistical analysis of the questionnaires. The results obtained show that community trust plays the most important role in the collaboration of researchers in virtual academic communities and that group interaction factors and individual factors also affect the cooperation of researchers in virtual academic communities. The conclusion suggests that the virtual academic communities need to establish and improve management norms and trust mechanisms, and also need to refine and improve the forum section and community incentives.

A Book Review on Sozioinformatik – Von Menschen & Computern … und Bibern [Social Informatics – About People & Computers … and Beavers]. Christa Weßel (Rastede: Weidenborn Verlag), 2021, 197 pages, ISBN: 978-3947287079.

Cryptocurrencies have become a major phenomenon in recent years. For IT, a breakthrough is both the cryptocurrency itself as a commodity and the technology that cryptocurrency development has brought. The article focuses on the bitcoin cryptocurrency as the most important cryptocurrency. A relatively unexplored topic is what goods or services are purchased for bitcoins. To track what bitcoins are spent on, it is necessary to look for places that are dedicated to trading cryptocurrencies. The bitcointalk.org forum was chosen as a source for our data mining. The aim of the article is to find an answer to the research question: What are bitcoins on the discussion forum bitcointalk.org planned to be spent on? As part of the research, an application was developed using a PHP script to gather information from the discussion forum (bitcointalk.org). There is some evidence which suggests what types of products or services people spend cryptocurrencies on. This research has proven that cryptocurrencies are used to buy and sell goods or services in the electronics and computer world segments. Today, these segments are widespread, which may speed up the integration of cryptocurrencies into everyday life. This applies, of course, only if the risks associated with cryptocurrencies do not increase.

This editorial aims to summarise the special issue entitled “Perspectives of Social Informatics” that builds on the current international view of social informatics. The special issue consists of eight scientific articles and one book review.

The prevalence of so-called “fake news” is a relatively recent social phenomenon that is linked to disinformation, misinformation and other forms of networked manipulation facilitated by the rise of the Internet and online social media. The spread of misinformation is among the most pressing challenges of our time. Sources from which disinformation originates are constantly changing and present an enormous challenge for real-time detection algorithms and more targeted science based socio-technical interventions. The primary aim of this paper is to illuminate the practices and interpretations, focusing on three perspectives: general attitudes to fake news, perceived interaction with disinformation and opinion on counteraction with respect to fake news. The innovative character of the research is achieved by the focus on community solutions to combat disinformation and the collaboration between media users, media organizations, scientists, communication managers, journalists and other important actors in the media ecosystem. Based on insights from interviews with communication field experts, the paper sheds light on the efforts of Lithuanian society to confront the problem of fake news in digital media environment. Lithuania is also an interesting case study for fake news due to its status as a former Soviet state now in the EU. Our research indicates that not all media users are prepared and/or have the necessary competencies to combat fake news, so that citizen engagement might actually negatively influence the quality of the counteraction process. Indeed, proactive citizens’ organizations and NGOs could be an important catalyst fostering collaboration between stakeholders. The responsibility of governments could be to create the structures, methodologies and supporting educational activities to involve the stakeholders in collaborating activities combating disinformation.

This note deals with the impact of the COVID-19 pandemic crisis on the ways how academic conferences are organized and run. Specifically, I reflect on my personal experience gathered during the attendance of the 21st International Conference on Agile Software Development, which was organized as a solely virtual event for the first time. First and foremost, I summarize the technical and other challenges encountered as a conference presenter and participant. Then, I shortly discuss a possible impact of the COVID-19 pandemic on the future publication standards in the sciences of computing.

There is a need to innovate current solutions in the field of fleet management and control systems in connection with the continuous growth of retail sales via the Internet and the associated delivery services. This is also supported by the development of the Logistics 4.0 concept, which refers to the massive digital transformation and deployment of cyber-physical systems based on artificial intelligence. For example, thanks to this delivery companies can reduce their operating costs, maintain competitiveness and margin size. The aim of this article is to present the basic principles on which the newly designed functionality of route and travel times optimization is based. This functionality is intended as part of intelligent transportation systems supporting delivery and utility services. The contribution of this article refers mainly to the field of practice. The article provides professionals with insight into the stated issue. Exemplary calculations of financial savings achievable by implementing such a solution in practice are also given.

Digitalization in human resource management helps enterprises to modernize HR functions and provides them with a competitive advantage. At the same time, it requires a change in working style and entails a change in the demand for HR competencies. The purpose of this paper is to introduce the phenomenon of digitalization in the literature, explore its current key benefits and risks, and analyse its influence on HR professionals´ competencies and roles. The qualitative research includes an analysis of the secondary data describing the existing level of digital skills based on the reactions of more than 7,000 respondents from six EU member states (Germany, Finland, United Kingdom, Portugal, Sweden, and Slovakia). The primary data analysis concerning HR social media competencies collected in five European countries which participated in the SHARPEN project is also presented here. The current trends in HR professional competencies are also described in this paper. The results obtained show that HR professionals tend to be slightly reluctant to adopt technology. The results also confirmed the importance of digitalization for human resources and the increasing demand for digital skills in recent years.

Even if we think that all the computer systems that are in operation work perfectly, the background might not be as it seems. We might face some faulty web interactions on a popular website or software as well. User behaviors are vital for developers in creating a satisfying computer system. in the aim of this study was threefold. Firstly, to determine if users’ tolerance of different kinds of faulty web interactions changes depending on the environment, and then to find how users’ behaviors differ when they encounter a faulty web interaction. Lastly, to detect how faulty web interactions shape users’ perceptions. To achieve these aims, we conducted a test on a manipulated mobile e-commerce website with 11 tasks including five faulty ones. Participants were not informed that the test includes faulty tasks. Faulty tasks consist of different kinds of web errors: Not Responding, Blank Page, Connection Timeout (HTTP-500), Not Found (HTTP-404), and Redirect (HTTP-301). The other tasks were organized as dummy tasks, and they were not examined. In the results of this study, we reached quantitative (for the collection of quantitative data, we used a Tolerance Evaluation Scale (TES) that we developed for this study) and qualitative findings. According to the quantitative findings, there is no difference between the tolerance levels of users for different environments. On the other hand, it was determined that when there is an error that includes feedback, user tolerance is affected positively. In addition to this, it can be seen that users have a low tolerance towards giving another chance to any kind of website which has a faulty interaction. In terms of qualitative findings, participants emphasized that it does not matter what purpose a website serves, the errors give an amateur impression by damaging usability and professionalism.