HCI in Business, Government and Organizations

The investment behavior of the lender becomes critical when a smart contract in combination with blockchain technology comes into play the role of intermediary to self-execute the transaction. Compare to general online lending platform where the lender have a high risk of bad debt, information asymmetry building of smart contract platform provide the solution to track historical transactions and immutable records. This study explores the impact of smart contracts on lender investment intention in online P2P lending platforms by combining the theoretical constructs of TPB and TAM. An experiment was conducted which collected 70 samples from two sub-groups of general online P2P lending platform and smart contract P2P lending platform. The preliminary results of experiment suggest perceived ease of use, and perceived usefulness have a significant impact in intention to use and investment by using smart contract embedded P2P lending platform. Both theoretical and practical implications of these findings are discussed.

In a traditional decision-making process, proposals are made and usually commented on by the participants, and finally a vote is taken. We have found in past public decision-making processes that there are also discussions about negative sides of proposals together with possible improvements of them. We have taken this as an opportunity to model the improvement of proposals within the decision-making process.Our new model for this is similar to version control systems like git work. Proposals in a decision process can have none, one or several predecessors. This structure allows different constructs of the real world to be modelled. Where previously only proposals could be made without reference and structure among each other, the system presented in this work allows modelling of further developments of proposals or even coalitions and compromises, in a real-time collaborative decision-making process.To validate our ideas, we tested them in two controlled experiments and concluded that our modifications are useful.

Digital transformation is a complex endeavor with unforeseen pitfalls. However, small and medium-sized enterprises (SMEs) seem to struggle with digital transformation. To address this problem, governments have started initiatives, i.e., publicly-funded support units, with the purpose to support digital transformation through facilitating idea management in SMEs. Studies suggest that the application of (IT-based) idea evaluation systems could impact the digital transformation success of SMEs. However, contributions addressing the interplay of support units and SMEs in the context of idea evaluation are scarce. Therefore, this article addresses the following problems (1) SMEs require external support in the context of digital transformation, and (2) idea evaluation is often improperly executed. Bringing together these areas leads to the objective to design and implement a collaborative idea evaluation system. The system development follows the methodological pathway for design science research. Observations from seven small-scale projects with SMEs complemented with selected literature inform the system design. Two focus group discussions serve as an evaluation of the system. The results are a set of design features and an interaction concept. Finally, a prototypical web application puts the conceptual design into action to tackle the identified practical problem.

In this paper, we aimed to aid the control measure that is implemented during the COVID-19 in Taiwan. As the virus spreads rapidly throughout the world, the Taiwanese government imposed three restrictions that help Taiwan to control the spread immediately. One of the restrictions that they imposed is to always wear a face mask. To avoid economic breakdown and still consider the general health of the public, Taiwan limits mass gatherings like in the food industry, entertainment, public transport, religious activities, etc. To be able to increase health security during a mass gathering, we developed an AI software to be able to detect people who are properly wearing a face mask, improperly wearing, and not wearing at all. The data that we used is from Kaggle to be able to use and process the data during image recognition, we use a raspberry pi board and camera. With the algorithm we used; we came up with an outstanding system where we could present excellent results due to the detection accuracy.

Local authorities are increasingly using online platforms as a means of involving citizens in urban planning and development. However, they encounter several challenges in the analysis and further processing of citizen-generated data when participation occurs on a large scale. Manual evaluation in particular takes individuals to their limits and hampers value extraction, e.g., for better planning and decision-making. To address these challenges, this paper presents a concept and design guidance based on elicited design requirements of an intelligent system that augments officials in data analytical tasks. Therefore, we focus on a hybrid solution to combine the respective strengths of humans and machines in data processing to mutually overcome their respective weaknesses. Particularly, our solution approach integrates human knowledge in the training process for machine learning via a Human-in-the-Loop strategy and simultaneously facilitates participation data analysis performed by officials. Promising initial evaluation results of the prototype indicate the usefulness of the approach.

The continuously growing speed of the digital transformation also impacts governments and public administrations worldwide. To keep up with this development, public officials are required to obtain and improve so-called e-competences. In order to determine developments in the perceived relevance of those competences over the past five years, we have set up a comparative survey study investigating (a) how the perceived importance of certain e-competences has developed (b) whether the supply of professionals holding certain e-competences has changed and (c) whether there is a change in public sector employees being sent to relevant training. The survey was filled out in 2016 and 2021. Out of 54 inquired variables, 29 show a statistically significant increase. From the employees’ perspective, we found a perceived increase in the importance and demand of e-competences and a decrease in having enough professionals available holding these e-competences.

The study aims to explore a virtual reality (VR) approach representing a local history in the sense of preserving cultural heritage. Two 400-year fort cities, Tamsui and Keelung, located in northern Taiwan, were chosen and transformed into digital fort cities by the researchers from the fields of history, information sciences, and computer science. The researchers invited participants to the VR lab and experience a VR tour. Research tools used for collecting user data include before and after attitude scales and a post-task interview for each participants. The findings of the research revealed that the senses of joy, presence, and control could critically impact VR user experience. The design of the content needed to be more informative and interactive. Affordances in the immersive environment were key for the user to experience a smooth and positive user journey with less pain points. For future work, the next round of data collection will be carried out. Data will be compared and contrasted further to see the differences.

The development of artificial intelligence (AI) attempts to model the human mind and processes. It has became clear that the interest of scientists in modelling human thought is inextricably linked to the brain, language, and physical body. Hilary Putnam is one of many researchers who have researched this area. His study called The Brain in the Vat has attracted a lot of attention for philosophers. The most important issue he raises in this article is the concept of the Brain in Vat, focusing on the development of brain, body, and language issues. The purpose of this paper is to analyze the causes of the Brain in the vat hypothesis and the interaction between the brain and the physical body for artificial intelligence. Since the argument is whether it is impossible to know if someone is the brain of a vessel. In principle, it is impossible to deny that you are the brain of the vessel, so there is no good reason to believe what you believe. The dubious argument argues that they cannot be known and raises issues related to the definition of knowledge. An example of this is the Brain in Vat hypothesis. It is necessary to study not only artificial intelligence scientists but also philosophers, neuroscientists, psychologists, medical scientists, physicists and many other scientists. It would be ethical to conduct human-centred artificial intelligence research in this way; always develop a link between artificial intelligence and the physical body in order to develop epistemological hypotheses in the philosophy of artificial intelligencee. In order to develop artificial intelligence, it is necessary to combine brain function and physical body interaction together. It is more important to develop and direct the activities of human-centred robots than to promote robots beyond the human mind. Although neuroscience is developing rapidly, not all the secrets of the brain have been revealed yet. Therefore, it is important to develop many interesting hypotheses from the philosophical and linguistic point of view for artificial intelligence. Many scientists have found that collaborative research is more likely to lead to better results. The development of human-centered artificial intelligence can bring positive technological and economic advances to humanity in many ways. The challenge is to direct and use this new evolution in a timely manner. When it comes to artificial intelligence, the question of the physical body is always on the agenda. Hilary Putnam’s Brain in A Vat is important not only to show many researchers what is possible and what is not possible but also to remind them what to focus on in the future.

Brand-related-user-generated-content allows companies to achieve several important objectives, such as increasing sales and creating higher user engagement. In this paper a research framework is developed that provides an overview of the necessary processes to successfully use brand-related-user-generated-content. The framework also helps managers to understand the main motives of users when posting brand-related-user-generated-content. Expert interviews were carried out to validate the research framework. The results from the interviews support the proposed framework. Brand-related-user-generated-content can increase purchase intention and the community engagement. From a user’s perspective the opportunity to interact with a brand and be featured on official brand channels could be seen as the main motivation for creating brand-related-user-generated-content.

Following the success of digital coins, such as Bitcoin, blockchain technology, on which digital coins are based, has attracted much attention. One of the main capabilities of blockchain is “to ensure any saved data is exactly the same as when it was generated” and that such data cannot be tampered with or destroyed. Therefore, the information written on blockchain is undeniable and absolutely credible. The cover story of the Economist in its October 2015 issue calls blockchain the “trust machine”. Related theories about the core capabilities of blockchain, i.e., “increases in information symmetry “ and “increases in mutual trust”, are used to construct the basis of an economic theory of blockchain to explore the potential application of blockchain and to consider incentives for the application of blockchain in marine debris removal in this research.Plastic debris leaks into the ocean and cause an unprecedented ecological catastrophe. This has become a hot topic for discussion amongst all disciplines. In an article published in 2016 by the Ellen MacArthur Foundation, which is committed to promoting the global circular economy, it stated that, at present, as much as 8 million metric tons of plastic debris leaked into the ocean each year, and if this continued, there would be more plastic than fish in the ocean around the globe by 2050. Following this shocking prediction, it was firmly declared in the G20 Osaka Summit, consisting of representatives from 20 nations, to reduce the environmental impacts caused by marine plastic debris “with a goal to end marine plastic pollution by 2050” and to collectively join actions in marine plastic removal.There are also many issues of information asymmetry and mutual trust in the governance of marine resource protection, for example, within a marine debris recycling system, the relationship between the manufacturing industry and marine debris, the increased cost of petrochemical plastic manufacturers in reducing marine debris, issues of trust in the government in promoting plastic reduction, the lack of the credit information of recyclers by cooperating financial institutions, the low willingness to participate by environmental recycling agencies in marine protection due to the high cost of marine debris removal, and issues of cooperation between the industry and the government in decision-making processes. These issues cause difficulties in introducing reform policies, such as, in respect of adjustments of guaranteed prices for marine debris recycling, and income insurance (assurance) policies for fishermen for their assistance in marine debris recycling. Promoting policies can encounter great difficulties as well, such as promoting policies in respect of adjustments of marine debris removal policies, classifications of marine debris recycling, marking systems for qualified recyclers for product conversions, and non-project based fishermen loans for marine debris recycling, etc. If blockchain technology can be used to solve the issues of information asymmetry and mutual trust among people, between manufacturers and consumers, among recycling agencies, manufacturers and fund providers, and between the people and the government, it will play a crucial role in introducing and implementing reform policies in marine resource conservation development. To demonstrate its determination in marine debris removal, the Ocean Affairs Council in Taiwan R.O.C. participated in the Presidential Hackathon 2020. With the theme of “Ocean Blockchain, Garbage Turning into Gold” and through the public-private collaboration, it proposed to use blockchain technology to connect volunteers and enterprises that would be willing to sponsor marine debris removal. In addition, based on the concept of a recycling economy of marine debris traceability, it was proposed that the collected information could be used in the future to establish hot spots for marine debris to call on all people for its removal, and, as a result, the quality of marine resource conservation could be improved to deal with the increasingly severe marine ecological destruction.

Emotion theories are mainly classified as categorical or dimensional approaches. Given the importance of emotional words in emotion research, researchers have constructed a co-occurrence corpus of 7 types of emotion words through word co-occurrence and big data corpora. However, in addition to the categorical approach, the dimensional approach plays an important role in natural language processing. In particular, valence has an important influence on the study of emotion and language. In this study, the co-occurrence corpus of 7 types of emotion words constructed by Chen et al. [1] was expanded to create a corpus of emotional valences. Then, stepwise multiple regression analysis was performed with the predicted criterion variables and 15 predictor variables. The criterion variables were the emotional valences of 553 frequently occurring stimulus words included in the Chinese Word Association Norms [2]. The predictor variables included the emotion co-occurrences scores for 2 clusters (a cluster of literal emotion words and a cluster of metaphorical emotion words) and 7 types of emotions (happiness, love, surprise, sadness, anger, disgust, and fear) [the emotional words were common words from both the co-occurrence corpus of 7 types of emotion words constructed by Chen et al. [1] and the Chinese Word Association Norms established by Hu et al. [2]] and the virtue word co-occurrences score. The results showed that the scores for literal happiness word co-occurrences, metaphorical happiness word co-occurrences, literal disgust word co-occurrences, literal fear word co-occurrences, and virtue word co-occurrences could predict the valence values of emotion words, with the multiple correlation coefficients of multiple regression analyses reaching .729. Subsequently, the valence values of 33,669 words were established using the formula obtained from the multiple regression analysis of the 553 words. Next, the correlation between the actual valence values and the predicted valence values was analyzed to test the cross-validity of the established valences using the common words in the norm established by Lee and Lee [3] for the emotionality ratings and free associations of 267 common Chinese words. The results showed that the correlation between the 2 was .755, indicating that the predicted values generated by the big data corpora and word co-occurrence had a degree of similarity with the manually determined values. Based on theories and tests, this study used the co-occurrence data of 7 emotions and virtue to construct the corpus of emotional valences for 33,669 Chinese words. The results showed that the combined use of big data corpora and word co-occurrence can effectively expand existing corpora that were established based on emotional categories, improve the efficiency of manual construction of corpora, and establish a larger corpus of emotional words.

Questioning and answering (Q&A) communities have become an important platform for online knowledge exchange. With a vast number of questions posted to elicit high-quality solutions as well as a large number of participants engaged in online knowledge sharing, a grand challenge for Q&A communities is thus to effectively and efficiently identify and rank useful questions. The current approach to solving this problem is either through user voting or by community moderators. However, such manual processes are limited in terms of efficiency and scalability, especially for large Q&A communities. Thus, automatically predicting the usefulness of questions has significant implications for the management of online Q&A communities. To provide guidelines for assessing the quality of online questions, this research investigates and compares various classical machine learning and deep learning methods for predicting question usefulness. A dataset collected from a large Q&A community was used to train and test those machine learning methods. The findings of this research provide important implications for both the research and practice of online Q&A communities.

Past studies used human rated as the way of establishing a corpus which costs a lot of time and money but contains insufficient words, also the Categorical Approach was seldom used for building corpus, which may also lead to study bias. Therefore, study 1 of present study has used the Spreading Activation Model as the structure, and used big data of text corpus and word co-occurrences to build a corpus that contains more categories of emotions and much more words. First, study 1 selected the words that can clearly describe the meanings or can effectively evoke the feeling of its emotion category for seven emotions, including Happiness, Surprise, Sadness, Anger, Disgust, Fear, and Love. Then study 1 calculated the averages of co-occurrences for selected words and text corpora by seven emotions categories (measure is Baroni-Urbani, unit is chunk), it computes the averages of co-occurrences by emotional categories for 33669 words, it represents the conceptual consonance of words and the emotions. Study 2 has investigated the practical use of the corpus built in study 1, and used C-LIWC dictionary which was built by human rated as a comparison, taking the posts of Happy Board, Sad Board, Hate Board of PTT Bulletin Board System into the analyses of emotions recognition, result showed that Corpus of 7 Types Emotion Co-occurrences Words” built in study 1 had higher correct rate than human rated corpus. Present study has also compared the correct rates between the Corpus of 7 Types Emotion Co-occurrences Words and CLIWC (Chinese Linguistic Inquiry and Word Count), result showed correct rates of two databases were significant different, the corpus of present study has higher correct rate. Present study has built a text corpus for the material of emotion research, and the results also supports a potential of building the corpora of emotional words with big data measures.

Accurately predicting carbon emissions and mastering the law of carbon emissions are the premise for effective energy saving and emission reduction and realizing the goal of “carbon peaking and carbon neutrality”. This paper takes foreign direct investment and environmental regulation as the influencing factors, and uses the nonlinear fractional-order grey multivariable model to predict carbon emissions interval. The results showed that foreign direct investment intensifies carbon emissions, while environmental regulation contributes to carbon emissions, with total carbon emissions still on the rise in the next few years. Paying great importance to the quality of “bring in” and making good use of environmental regulation is an important way to achieve sustainable development.

Due to the rapid progress of digitization and the increasing importance of modern information technologies like collaboration platforms companies need to find appropriate ways to overcome the looming qualification lag of their employees. Aggravated by the demographic change, they have to ensure that the knowledge of older, experienced employees is made available to others to prevent its loss in case of retirement. This is especially relevant for small and medium-sized companies where deep personal knowledge is crucial to maintain competitiveness [6]. In this context, it is of paramount importance to enable employees to document their knowledge without interrupting their daily work routine. Knowledge acquisition, distribution and utilization have to be seamlessly integrated into the employees’ daily operations. This paper presents a graph-based approach to visualize assembly knowledge and its prototypical implementation. The resulting knowledge management system has been evaluated within one medium-sized company in the domain of mechanical engineering. These results as well as future developments are discussed at the end of this case study.

Cardiovascular diseases (CVDs) have been reported as one of the leading causes of death worldwide by World Health Organization (WHO). Although CVDs can be treated, it has high risk of recurrence. In this study, we intended to construct the predictive model of cardiovascular disease recurrence by machine learning approach. We used the 18-month prognosis tracing data to construct and evaluate the recurrence predictive model. We collected 36 physiological factors associated to the cardiovascular disease recurrence identified from literature from 1274 cardiovascular disease inpatients as they discharged from the hospital and their follow-up prognoses after six months. To address the imbalance data problems that are prevalent in medical dataset, we revised the ensemble learning method by performing multiple undersampling to construct a committee of SVM classifiers. The evaluation results show that our proposed approach outperforms all benchmarks in term of F1 measure and Area under ROC curve (AUROC). Our study has demonstrated an approach to address to construct an effective prediction model for cardiovascular diseases recurrence. It might also support physicians in assessing patients who are at the high risk of recurrence.

Effectiveness of advertisement can be measured in terms of a person’s emotional response to the advertisement media. Besides traditional survey method, the emotional feedback of the consumers is valuable to understand the purchasing intention. Artificial intelligence and machine learning have provided researchers and practitioners of marketing and advertisement with new tools, such as facial expression recognition analysis, to explore the context of advertisement effectiveness. In this study, a facial expression recognition survey was carried out for analyzing the advertising impact and consumer feedback on types of advertisement. Participants were separated into two groups, where the first group was asked question related to one randomly chosen digital poster advertisements of green products and the second group was surveyed for an advertisement without sustainable properties. The facial expressions of participants were recorded and later classified into three states as positive, neutral, and negative using a machine learning model. The results reveal people show relatively higher positive emotion for green products and their purchasing intentions are driven by the willingness to save environment and perceived value. Whereas, for normal products, purchasing intentions are mainly driven by the brand image. This study also shows that sustainable cues in product advertisement leads to positive consumer feedback.

Overcrowded conditions in emergency departments (EDs) have increased patients’ waiting time, while the variety of patient afflictions has caused difficulties in the allocation of medical resources. Therefore, the ability to predict a patient’s hospital admission at the time of triage could allocate medical resources to patients who go to EDs in urgent need of immediate care. Using a dataset from the MacKay Memorial Hospital in Taipei (Taiwan), which contains 177,038 valid records collected from 2009 to 2010 in this research, we aim to have on hand chief complaints (CCs), demographic data, administration information and clinical information at the triage stage to predict the probability of a patient’s hospital admission. Firstly, we select terms from the CCs to predict which patients may require eventual hospitalization. We then integrate the selected terms with several algorithms to predict the probability of patient admissions. Accordingly, this research includes a series of machine learning processes, such as data preprocessing for structure data and CC data, imbalanced data processing, models construction by logic regression, neural networks, random forest, XGBoost, and model evaluation. The research results show that the ensemble learning approach, XGBoost, can achieve 0.88, and 0.76 in terms of accuracy and AUC respectively. The results show that triage, fever status, age, and terms extracted from the CCs are important attributes to predict if patients should be hospitalized. The results of this study will provide a reference approach in the field of emergency hospital admissions prediction and help hospitals improve resource allocation in emergency rooms.

Hand gestures are a relatively new way for humans to communicate with computers. The goal of gesture recognition is to bridge the physical and digital worlds. Hand gestures make it much easier to communicate our intentions and ideas to the computer. There are numerous methods for a computer to recognize a hand gesture, one of which is image recognition. The use of a Convolutional Neural Network (CNN) allows for the detection of human gestures. However, training a CNN necessitates a massive dataset of human gesture images. In this paper, we employ Google MediaPipe, a Machine Learning (ML) pipeline that combines Palm Detection and Hand Landmark Models, to develop a simple hand tracking method to control a Robot Operating System (ROS) based surveillance car with socket programming. The study demonstrates control of a ROS car’s steering direction and speed. Hand-gesture-controlled surveillance vehicles could aid in the improvement of security systems.

Voice user interfaces (VUIs) offer an intuitive, fast and convenient way for humans to interact with machines and computers. Yet, whether they’ll be truly successful and find widespread uptake in the near future depends on the user experience (UX) they offer. With this survey-based study (n = 108), we aim to identify the major annoyances German voice assistant users are facing in voice-driven human-computer interactions. The results of our questionnaire show that irritations appear in six categories: privacy issues, unwanted activation, comprehensibility, response quality, conversational design and voice characteristics. Our findings can help identify key areas of work to optimize voice user experience in order to achieve greater adaptation of the technology. In addition, they can provide valuable information for the further development and standardization of voice user experience (VUX) research.

Especially in e-commerce and associated online marketing, web cookies play an essential role as they provide information that is key, for instance, to improving website functionality and customization. With the 2019 ruling of the Court of Justice of the European Union, cookie notices became mandatory in the EU. Companies seek to measure and improve cookie opt-in rates to avoid large data losses relevant for online marketing. We tested in an experiment the most common cookie variants – the binary-choice cookie notice and the category-choice cookie notice – for their acceptance rates. The results showed that the former achieved a slightly, but statistically significantly, higher opt-in rate, and the highest opt-in rate was found among users browsing on mobile devices. The decision to accept or reject cookies when presented with a cookie notice is made within seconds and can be influenced by various external factors, which we sought to identify and examine in this study with the use of a survey following the experiment. None of the external influencing factors examined were perceived as influential by more than half of the participants. Simplicity of use, the speed with which the cookie notice is dismissed and time pressure when browsing were the most frequently mentioned external influencing factors. However, all factors examined had some effect on users’ attitudes to cookie notices.

This study conducted an empirical survey to investigate the factors influencing investors’ willingness to use Robo-advisors. The research results show that both perceived ease of use and perceived control have a positive and significant impact on perceived usefulness, which, in turn, increases the willingness to use Robo-advisors. Social presence also positively affects users’ willingness to use Robo-advisors. Moreover, users’ trust in vendors increases users’ trust in Robo-advisors, while perceived risk decreases trust in Robo-advisors. Additionally, users’ anxiety enhances users’ perception of risk. The findings show that users’ trust both in vendors and in Robo-advisors positively affects users’ willingness to use Robo-advisors. This study provides useful insights into the marketing strategy of Robo-advisor services.

Building Information Modelling (BIM) applications are widely used in construction industry. Since there is some resistance to their adoption, we have to understand better the process of their acceptance and adoption. We introduce a three-legged approach to social acceptance assessment consisting of three main phases: The first leg/phase introduces a case-based approach for Adoption Impact Map construction, in which the aim is to model impacts of BIM adoption on end-user value. In the second leg/phase, BIM adoption among stakeholders is evaluated through the SPHERE BIM Digital Twin adoption survey, which will be distributed among professional stakeholders at the ending phase of the project. The survey is divided into two main parts: measurement of BIM acceptance readiness and measurement of individual and organizational intention. In the third leg/phase, post-occupancy evaluation is implemented through the SPHERE post-occupancy evaluation questionnaire. It gathers feedback on what occupants like in the building in which they live. The results of the assessment will be used to further support the development of SPHERE BIM Digital Twin Platform tool.

As a university it is more and more difficult to reach all target groups equally. Common problems like information overload, numerous institutions with same focuses or multi-channel-communication make it hard to gain the attention of the target group. This paper is four-fold: we present an overview of the state of art and the importance of the study (I), based on which we highlight the approach to user experience analysis. First, we identified the irritations in the course of an expert evaluation (II) and verified them within the test, including the target groups (III). Finally, based on the results, we were able to pro-vide recommendations for action to improve the UX and to be used for the conception of an intranet (IV).

As more human needs are addressed with technology, designing positive user experiences becomes increasingly important in developing effective health interventions. Designing successful user experiences for digital health interventions requires a deep understanding of patient challenges. In this paper, we attempted to identify challenges that diabetic patients face adhering to guideline-recommended care through persona development. Previous user experience research suggests that such an approach can be particularly beneficial in designing digital health interventions. We explain how we developed data personas from Electronic Health Records (EHR) and combined them with proto personas that were generated by a group of medical experts for the same patient population. Our results support previous research that suggests combining data and proto personas is beneficial for intervention design. Additionally, our results reveal that combining data and proto personas is likely to improve intervention design by addressing fairness issues that may result from the underrepresentation of certain populations in EHR datasets.

This research provides insight into applying self-service checkout kiosks in supermarkets to create innovative service experiences based on gender. A self-service checkout kiosk is essentially a device that allows consumers to interact directly with a supermarket, receive service at their convenience, increase revenues and streamline all purchasing processes. This innovative technology allows customers to have a better user experience without queues. To know the gender difference of self-service checkout kiosks’ innovative service, this study began with the user-centered process that integrates emotional design and user experience design to analyze the supermarket’s application scenarios of self-service checkout kiosks. Then we proposed eighteen innovative functions to create better service experiences for customers. We use Kano’s Model method to design a questionnaire and invite candidate users to evaluate the proposed innovative functions by converting each respondent’s answer to a score of “satisfaction potential.” The Kano’s Model evaluation results are then classified the proposed innovative functions into must-be, one-dimensional, attractive, and indifference categories and show gender differences. The results of this research show that gender does affect system performance and users’ satisfaction classification, priority, and user experience to the proposed self-service checkout kiosk innovative functions. The results also show a comprehensive analysis of the gender difference in the innovative function design of supermarket self-service checkout kiosks. The contributions of this research can not only be used by the stakeholders of supermarkets to draw their strategies for deploying self-service checkout kiosks but also provide a better user experience design to the supermarket customers. Thus, in future designs, designers can refer to the results of this study to design self-checkout kiosks for offline retail shops based on brands with different positioning.

In this paper we present a gamification process developed in a Brazilian public institution. We used gamification to engage users in the reports’ quality improvement of the organization. Gamification is the application of game elements and game principles in non-game contexts [1]. The Octalysis framework proposed by Yu Kai-Chou [2] was used to model the users’ profile and the gamification itself. Based on the identified profiles, a gamification was created using the techniques of Classification, Medals, Level Up, Attachment Monitor, Mentoring and Build from Scratch in addition to the techniques of Countdown, Grave Tombstone and Group Challenge. In new gamified process, the players interact collaboratively and develop skills and competences over the same shared space. The result shows that use of the gamified process both enable a better reports’ production and creates a fun and attractive way to improve learning, affective, sociocultural aspects, collaboration, and improvement at the organizational model.

Workplace violence (WPV) is severely underreported among healthcare workers (HCWs) who are four times more likely to experience WPV compared to professionals in other industries. The frequency in which HCWs are exposed to acts of WPV prompts urgency in the development of improved reporting tools to capture HCWs’ experiences more adequately and provide evidence for strategic improvement initiatives. Techniques in human-centered design and product discovery techniques can enhance the redesign process and help teams identify what HCWs value and deliver value, faster.Purpose: To explore how human-centered design and product discovery techniques can be used to inform feature recommendations for a minimum viable product (MVP) for an improved WPV reporting tool for HCWs.Methods: We used a mixed-methods approach based on human-centered design and product discovery techniques.Results: Two distinctive themes emerged, informing our design objectives: (1) To increase reporting of WPV and (2) to provide access to WPV support resources. The recommended set of product features was well-received among HCWs.Conclusion: Human-centered design and product discovery techniques can be used in a complementary fashion to design WPV reporting tools that better align with the values of HCWs.

Use of electronic monitoring techniques in organizational settings has significantly gained in relevance in recent years, predominantly due to the increased availability of corresponding information and communication technologies. Moreover, the restricted social mobility due to COVID-19 and the resulting increase of home office has come along with an increased interest of employers to monitor the activities of their employees. Against the background of these developments, along with the general importance of theory-focused research, the goal of this review is to identify the theories used in the electronic performance monitoring (EPM) literature and to illustratively show how recently identified research gaps could be examined in future studies based on theory integration. A total of twelve theories were extracted from the literature and examined. Four of these theories were integrated in a conceptual framework. In addition, a study design for future research is outlined. The overall objective of the present contribution is to strengthen the theoretical foundation of the EPM research field.

In response to the COVID-19 pandemic, the Exascale Computing Project’s (ECP) Interoperable Design of Extreme-scale Application Software (IDEAS) productivity team launched the panel series Strategies for Working Remotely to facilitate informal, cross-organizational dialog in the absence of face-to-face meetings. In a time of pandemic, organizations increasingly need to reach across perceived boundaries to learn from each other, so that we can move beyond stand-alone silos to more connected multidisciplinary and multi-organizational configurations. The present paper argues that the unplanned transition to remote work, overuse of electronic communication, and need to unlearn habits associated with an overreliance on face-to-face, created unique opportunities to learn from the situation and accelerate cross-institutional cooperation and collaboration through online community dialog facilitated by informal panel discussions. Recommendations for facilitating online panel discussions to foster cross-organizational dialog are provided by applying the Simulation Experience Design Method.

The Industry 4.0 paradigm requires not only equipping the shop floor and the workforce with new digital tools, but also ensuring that digital technologies are well accepted and adopted by workers. This is achieved through the active involvement of those who will be affected by these new digital technologies. However, the involvement of end-users and workers in the design process is often confined to test fully developed solutions. Rarely are the workers fully involved in the design process, from preliminary research to the co-design of the proposed technologies. As such, and in order to involve workers on the design of a digital companion for shop floor operators, we applied a Participatory Design approach, with mixed-methods, consisting of fieldwork observations, interviews, and exploratory workshops with workers and other relevant stakeholders in order to understand their needs and desires in the context of Industry 4.0. These activities were conducted remotely and in situ, and aimed to identify issues concerning the worker, the workplace, and concerns regarding their physical and mental well-being. In this paper, we describe the process of designing the worker companion-the methods, main insights from the user research activities, and an initial prototype.

In the development and research of IT-supported systems, the user is increasingly in the center of attention. Therefore, we investigated the approach of how beneficial it would be to use tangibles (physical objects) as an interaction option for the user at an assemble workstation. For this purpose, we designed an augmented reality concept for the use of tangibles at a manual assembly workstation and evaluated it with an eye tracking study. Through the study, we found that the participants were very comfortable with the tangible interaction concept, learned very quickly, and found it very easy to use. Further, we present how it is possible to improve assistance systems, such as pick-by-light systems, with the use of augmented reality and tangibles. For this, we show that the use of tangibles offers many advantages like training new employees fast at an assembly workstation.

In response to increasing growth rates in online retail and changing consumer behavior, many retailers are pursuing an omni-channel strategy. Smart retail technologies, such as smart fitting rooms, help to integrate online and offline channels and to create a strong, holistic customer experience.This research investigates the drivers and barriers regarding the use of smart fitting rooms in German fashion retailing by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by the variables ‘need for interaction’ and ‘willingness to provide personal information’. ‘Age’, ‘gender’ and ‘experience’ were examined as moderator variables. Data was collected using a quantitative online survey and analyzed by means of regression analysis.The most significant and substantial factors influencing consumers’ intention to use smart fitting rooms proved to be ‘hedonic motivation’, ‘performance expectancy’ and ‘willingness to provide personal information’. The variables ‘effort expectancy’ and ‘facilitating conditions’ have a weak significant influence on the use intention. ‘Social influence’ and ‘need for interaction’ did not prove to be influential in this study.The examination of moderator effects showed that ‘age’ only moderated the influence of ‘willingness to provide personal information’ while there were gender differences for ‘performance expectancy’ and ‘hedonic motivation’. The results also show that, especially the predictor ‘facilitating conditions’ has a much larger effect for inexperienced users.

Live steaming become an emerging significant phenomenon for creating huge market value in business. When it is reposed successful stories about the live streaming economy, researchers are interested in how a live streaming can be successful. Current studies explore this issue by investigating the motivations that can drive audiences watching a live streaming. However, it remains unclear about how and why the liver streamers perform particular practices to attract audiences and manage their live streaming. This study bridges this gap by applying the dramaturgical theory to explain the process where live streamers manage consumer’s sensemaking process with image creation. The dramaturgical theory regards individual daily behaviors as ‘performances in front of others.’ Specifically, an individual is like an actor, who manage her image by performing certain behaviors in front of people. This theory provides an opportunity to analyzes the live streaming practices by revealing the live steamers’ understandings that constitute and support their ‘performances’ in the live streaming platform. Our findings identify three types of performance, namely idols, master players, and market seekers. Our analysis discusses the three types of performance by describing the live stream understandings on the stage (i.e. the live streaming industry), on the audience they faced, and on the roles they played (i.e. as live streamers) to unfold the logic embedded in live streaming practices. Our findings can contribute to deepen current understanding on live streaming practices and provide insights to aim live streamers to leverage audience’s sensemaking for image creation.

In the era of consumer upgrading, people’s consumption psychology and consumption behavior are different from traditional ones. Traditional brick-and-mortar operations or traditional e-commerce sales methods can no longer meet consumer needs, and new retailing has emerged. New retailing is an innovative retail model that is still in the process of exploration and needs to be guided by more scientific and reasonable theories. Service design is an effective methodology and strategic tool across all industries, and its focus is consistent with the focus on user needs in new retail. Therefore, introducing service design in the new retail field to gain insight into user needs and reframe problems or opportunities can achieve comprehensive customer experience and employee experience improvement, and make the new retail industry more systematic and standardized. This paper explores and investigates a better new retail service system model from the perspective of service design. The necessity of combining service design factors with new retailing factors is explored, and the breakthrough point of combining the two is found. On this basis, new retail design principles and design methods based on service design concept are proposed. Taking electronic product recycling service system as an example, this paper focuses on the service design strategy and new retail model of electronic product recycling system. Through visiting observation method, literature search method, comparison analysis method and other research methods, this paper proposes corresponding design strategies for existing problems such as weak user awareness of recycling, high degree of regional demand differentiation and inefficient transaction mode, and carries out design practice.

Humanizing customer service chatbots have sparked significant interest for companies across industries. These years have witnessed some controversy on trust issues of such booming application. Previous researches have proposed some antecedents of customer service chatbots adoption (e.g., anthropomorphic features, algorithm aversion, emotional state). However, consumers’ trust mechanism and trust boundary on humanizing customer service chatbots are not clear. Hence, we pay attention to personalization and contextualization grounded on above antecedents of customer service, incorporating personal habit, task creativity and social presence to investigate trust mechanism and trust boundary. We propose a research model, in which personal habit and task creativity are captured as independent variables, trust in humanizing customer service chatbots as dependent variable, and social presence as moderating variable. Hypotheses are developed and between-subjects scenario experiments are conducted to test hypotheses. Results of analysis of covariance (ANCOVA) and moderating effect test show that there exists positive effect between personal habit and trust in humanizing customer service chatbots, giving insights on complementary and substitutive influences on the interaction of independent variables and social presence for trust boundary. This paper provides practical and theoretical implications for e-commerce practitioners to improve the collaboration performance of intelligent customer service and human customer service.

During the COVID-19 pandemic, many governments restricted economic activity by imposing lockdowns or requiring capacity constraints, thereby impacting brick-and-mortar businesses. Consumers responded by staying at home and turning to online shopping. Some consumers were already familiar with online shopping, whereas for others it was a new experience. As restrictions are removed or reduced, consumers may permanently change their shopping habits and continue to buy online with greater frequency than prior to the pandemic. With empirical data from a cross section of Canadian and American consumers, this study investigates the factors that influence the continuation of online shopping. The results show that there is little difference between Canadians and Americans, with perceptions of convenience significantly influencing perceived usefulness, and efficiency being a significant factor as well but only for Americans. Perceived usefulness is important for continuance intentions, with hedonic motivation having a moderating effect. Our results provide guidance to practitioners who are interested in consumers’ online shopping intentions after the pandemic and factors that can foster such activities.

Nowadays, many young adults who become independent lack the necessary financial knowledge and experience to succeed. This paper introduces a service called ListSmart which is aimed at helping young people create and manage shopping lists, save more on their purchases, and give them a seamless shopping experience in physical stores. Due to their lack of experience, they spend and save money inefficiently. We seek to design a smart shopping list application that can influence millennials and Gen Z and encourage them to build healthier financial lives. Through a user-centered design approach, we researched young adults and their financial behavior by reviewing existing literature, sending two rounds of surveys and user evaluations. From our research, we have created four main features: intelligent shopping suggestions, budgeting capabilities, the ability to link with local stores, and a list-sharing platform. With these features, we seek to explore how smart shopping lists budgeting tools can improve the financial lives of young people in the modern age.

The concept of customer cocreation has emerged as a key advantage for companies wishing to maintain or gain competitiveness. In the Web 2.0 context, bidirectional communication flow creates a conversational environment in which people can share their ideas and opinions with companies and others. This study explored the use of visual interfaces and gamified elements in the development of online platforms for engaging users in providing their opinions. A total of 134 participants were recruited. The findings indicated that the participants’ engagement and experience with a platform influenced their intention to participate in additional cocreation activities in the future and that gamified elements enhanced the participants’ experience with and motivation to engage in cocreation activities. A visual interface in a platform can effectively guide and support users in cocreation activities. Furthermore, gamification can enhance the hedonic value of the cocreation experience and strengthen intention to participate.