HCI in Business, Government and Organizations. Information Systems and Analytics

Smartphones are the most relevant digital companions that we employ on a daily basis and increasingly used by employees to perform business tasks. However, some companies do not allow the use of personal devices to access their IT infrastructure (BYOD) due to security concerns or for other organizational reasons; thus these employees have to use a smartphone provided by the company. Hence, employees may be confronted with unfamiliar operating systems or applications and which may influence their satisfaction and performance. We analyzed these effects by conducting a usability study that measured efficiency, effectiveness, and satisfaction of iOS and Android users when required to use a company provided device (CPD) in the form of a Nokia Lumia Windows Phone compared to their own device when executing the same tasks on their own device. The study used a within-subjects design and three measurement points, each of which consisted of multiple typical business tasks. The study used a within-subjects design and three measurement points each, which consisted of multiple typical business tasks. Results show that users become accustomed to the Company Provides Devices fast, since efficiency and effectiveness improve rapidly. The satisfaction level also improves, but remains below the personal devices.

It has been cogently acknowledged that employing BIM in the built environment companies has delivered remarkable benefits such as enhanced HCI, superior visualization, precise documentation, integrated design, construction and project management processes. Yet, the Architecture, Engineering and Construction (AEC) enterprises involved are still lagging behind in embracing BIM into core practices of their projects. This is particularly evident in the case of Small and Medium Sized Enterprises (SMEs) where higher levels of BIM implementation need to be scrutinized. There is little evidence on how these SMEs perceive the role of BIM management, and to some extent, they apply this process in their projects. The limited financial and human resources of these SMEs make it difficult to keep up with such BIM adoption processes. Therefore, to address these challenges, this paper is to shed light on the potentials of applying the business strategy of Multi-Sided Platform (MSP) in the construction industry and adapting its conceptual model for managing BIM implementation in construction SMEs. Positioning BIM professional services in MSP model can enable these firms to focus on their core businesses while benefiting from the senior talents which offer immediate access to BIM industry best practices. The study contributes to the field by providing succinct information on MSP implementation and its adoption in AEC SMEs. The study contributes to the body of knowledge through positioning BIM management platform in a rather overlooked context namely SMEs. Practically, policy makers and stakeholders would also benefit from the findings in order to promote BIM adoption.

The utilization of information technology has altered the basic nature of industry. Information technology also changed its traditional role from back office to a strategic role. The strategic use of information technology has been realized as a fundamental issue for business. The fast information technology development and its adoption in the business organization in recent years, for examples, the use of blockchain, or Internet of Things in business has brought an information systems strategic change in the organizations. This paper explores the impacts of information systems flexibility on information systems strategy implementation in a Chinese multinational State-owned Enterprise. The research design for the study follows a rigorous grounded theory approach, which consisted of 41 semi-structured interviews in 7 different company branches in China. Based on the study, we propose five main categories of information systems flexibility impacts on the information systems strategy implementation. Our study contributes to the new information technology adoption literature and provides implications for information technology adoption in practice.

Due to intricate network in industry business and high cost of supervision, financial institutions usually focus on supervising core enterprises in a supply chain instead of all corporations, which indirectly lower the strength and efficiency of financial institutions as a role of capital supervisor and credit-risk transformer. Furthermore, banks require these corporations to provide correct information by themselves, which lacks of the objectivity of the source information and increases the supervision cost for these banks. Thus, we summarize a company relation detection task in hope to exposing more information about companies to investors and banks by learning a system from public available datasets. We regard this task as an classification problem, and our system can predict relations between any two companies by learning on both structured and unstructured data. To the best of our knowledge, it’s the first time to implement deep learning technique to this task. A F1 score 0.769 is achieved from our system.

Information visualizations of quantitative data are rapidly becoming more complex as the dimension and volume of data increases. Critical to modern applications, an information visualization is used to communicate numeric data using objects such as lines, rectangles, bars, circles, and so forth. Via visual inspection, the viewer assigns numbers to these objects using their geometric properties of size and shape. Any difference between this estimation and the desired numeric value we call the “visual measurement error”. The research objective of this paper is to propose models of the visual measure error utilizing stochastic geometry. The fundamental technique in building our models is the conceptualization of eye fixation points as might be determined by an eye-tracking experiment of viewers estimating size and shape of a visualization’s object configurations. The fixation points are first considered as a stochastic point process whose characteristics require comment before proceeding to the statistical shape analysis of the visualization. Once clarified the fixation points are reinterpreted as a sampling of the shape and size of the landmark configurations of geometric landmarks on the visualization. The ultimate end of these models is to find optimal shape and size parameters leading to minimum visual measurement error.

To stay competitive in marketplace, companies have to design compelling products that are successfully adopted by their intended users. To achieve this goal, companies must establish and validate a shared understanding of their customer base. In this paper we propose a persona development process for creating value in an organization. This process, which can be conducted relatively quickly and cost-effectively, provides a roadmap for continual assessment of organizational understanding of customer base. It creates value for companies by allowing them (1) to build consensus internally around their customers’ needs, goals, and preferences, and (2) to validate/test the accuracy of the assumptions they make about their customers. We tested this process in a fortune 500 company. Our results suggest that our process is both effective and efficient in establishing a shared understanding of the customer base and in testing the accuracy of assumptions made by organizations about their customers.

There is substantial academic interest in modeling the determinants of mobile apps’ success. However, few relative researches explored the impact of development strategies and market status of mobile apps on their market performance. This paper adopted text mining technique and Boston Consulting Group (BCG) Matrix to measure the divergence of a development strategy and market status, respectively. Furthermore, we construct a multivariable linear regression model of performance of apps using data from five mobile platforms: Mumayi, Baidu mobile assistant, 360 mobile assistant, Eoemarket, and App China. The result shows that apps of Stars require convergent development strategies to attract potential consumers while more generally, the divergent development strategies benefit apps in other quadrants of the BCG Matrix, namely Cash Cows, Problem Children and Dogs.

This work investigates the potential bivariate correlations between selected pattern related mouse attributes and a set of factors for the determination of the satisfaction with the usability. To examine this, a prototype tool for the analyzation and characterization of mouse attributes, Simple Mouse Attribute Analysis (SMATA), within the usage of a cloud-based vertical business software solution for managing soft data, was designed and implemented. A questionnaire was conducted to evaluate the users’ satisfaction with the usability. Following, the potential correlation between those properties was investigated. The findings revealed several statistically significant correlations between the factors of satisfaction with the usability and the examined mouse attributes. Mouse attributes like the number of direct movement, the number of long direct movements, the number of made pauses, as well as the covered distance and the total time of the session could be associated with the perception of the system usefulness, the information and interface quality and the overall impression. The objective of this study was to point out a new interesting research direction of using implicit gathered user data from one of the default communication channels in HCI: the computer mouse.

A car is only useful, when it runs properly – but keeping a car it running is getting more and more complex. Car service providers need a deep knowledge about technical details of the different car models. On the other hand car producers try to keep this information in their ownership. Digital data collection takes place every second on the car’s product life cycle and is stored on the car producers’ servers. The contribution of this paper is three-fold: we will provide an overview of the current concepts of intelligent order assistant technologies (I). This corpus is used to come to a more precise description of the specific service performance aspects (II). Finally, a representative empirical study with German motor mechanics will help to evaluate the wishes and needs regarding an intelligent order assistant in the garage (III).

The need to create a set of Process Best Practices specifically for Foundational User Experience (UX) Research becomes essential in driving an increase in the success of this type of research, and the continued positioning of UX Research as a strategic partner contributing to ongoing product strategy and business success. These Best Practices have been crafted by author’s over 17 years of professional UX Research & Strategy experience across a variety of industries (medical, mobile, printing, IT) and companies (Baxter, Motorola, HP, Google); and they will continue to evolve as emerging technologies and new methodologies continue to impact the UX Research landscape. These foundational projects have varied in scope, depth, and objectives; however, the following of these Best Practices has remained consistent and contributed to many successful and impactful projects over the years. The results of these research projects have directly impacted entire business units, driven the future direction of product portfolios, pivoted product strategy, and created new business opportunities. The Process Best Practices described in this paper are designed to successfully guide a research practitioner step-by-step through a Foundational UX Research project from initiation through insight integration.

This paper presents the model for the competitiveness of soccer matches played in the top four European soccer leagues. Every soccer match in every league holds some importance and contributes towards the overall performance of the league compared to other leagues. These individual results constitute a single season. A lot of aspects of a team and a season are attributed to their final positions in the league. These positions, however, do not detail the competitiveness of a single match. This research aims to highlight the competitiveness in each match without any relation to how the season may have ended. A match gives out a lot of details towards how it was approached by a team. A win may not constitute competitiveness, but the approach does. The idea is to look at individual statistics of a match and use them to construct a model using SEMMA approach of data mining, that classifies the matches based on how competitive they were. This research constructs various models for classification as each model provides its own variant based on the different methodologies used in the individual models. Our analysis is mainly depended on, but not limited to, the number of attempted shots on goal and on the number of those shots that were on target. An important characteristic of the attempts on goals is that they are subjective to the performance of a team and its ability to try and secure a win in a match. This performance formulates competitiveness which is the basis of our research.

Mergers and acquisitions (M&A) happens frequently between corporations to combine and/or transfer their ownerships, operating units and assets. The purpose of the study is to develop a service that is able to recommend a feasible M&A deal. We integrate the support vector machine model with the kernel tricks to automatically determine M&A deals. In the end of the study, our proposed technique is empirically validated, and the results show the effectiveness of the recommendation service.

The Privacy Paradox is an information privacy behavioral phenomenon wherein individuals are aware that the personally identifiable information (PII) they disclose in an online transaction may be compromised, yet disclose it nonetheless. One explanation that has been given for this phenomenon is that the decision to disclose information online is informed by a risk/reward analysis, referred to as Privacy Calculus. However, the broad privacy calculus framework does not necessarily provide insight into specifically how an individual assesses either risk or reward. In our study, we evaluate several behavioral factors in an effort to assess whether and to what extent each informs or influences an individual’s risk assessment when deciding whether to disclose or withhold their PII in a given online transaction. Specifically, we report findings from a recent survey we administered, examining factors included in three different behavioral models. Results from this survey were analyzed using exploratory factor analysis, which provided insights as to the salience of each variable vis-à-vis online information behavior. Of the factors included in our study, our results surfaced four variables – perceived trustworthiness, perceived vulnerability, “cyber” self-efficacy, and perceived controllability – that appear to be particularly salient in an individual’s decision to withhold or disclose PII online.

Health consumers engage in social interactions in online health communities (OHCs) to seek or provide social support. Automatic classification of social support exchanged online is important for both researchers and practitioners of online health communities, especially when a large number of messages are posted on regular basis. Classification of social support in OHCs provides an efficient way to assess the effectiveness of social interactions in the virtual environment. Most previous studies of online social support classification are based on “bag-of-words” assumption and have not considered the semantic meaning of words/terms embedded in the online messages. This research proposes a classification framework for online social support using the recent development of word space models and deep learning methods. Specifically, doc2vec models, bag-of-words representations, and linguistic analysis methods are used to extract features from the text messages that are posted in OHC for online social interaction or social support exchange. Then a deep learning model is applied to classify two major types of social support (i.e., informational and emotional support) expressed in OHC reply messages.

Big data analytics is emerging in many industries and is a prominent undertaking in healthcare. The healthcare industry has more opportunities now to garner insights and advantages from data than ever before. Big data analytics has the potential to enhance many aspects of the industry from enhancing quality of patient care to revenue cycle improvements. However, successfully leveraging big data analytics poses challenges as well, such as data standardization and integrity. Therefore, it will be important to identify the essential factors that will facilitate the ability to derive the maximum value from big data analytics for such endeavors. This research proposes to identify these pivotal factors of big data analytics in healthcare by utilizing value-focused thinking (VFT). VFT will entail interviews with both healthcare data analysts and management to identify these important factors. The findings can provide guidance to practitioners when considering the essential factors for big data analytics success, as well as provide topics for future research.

The electronic medical records (EMRs) contain information about the patient such as their date of birth and blood type as well as other medical information such as prescription history and previous syndromes. Physicians usually have limited time to identify critical information on medical records and to provide a summary before they make a decision. However, the content of EMRs usually be complicated, repeated, and contain many consistency problems; these issues are not only cost a lot of time for physicians to filter information out from the medical records but also increase the probability of wrong medical decisions. Therefore, this study proposed a new EMR interface to identify the new medical information such as new syndromes or the turning point in the medical records. The Metathesaurus database which contains medical information such as medical terms or classification codes in the Unified Medical Language System will be used. This study uses MetaMap tools to compare medical terms within EMRs using MetaMap and also compares the vocabulary using the bigram technique to highlight the similarities in the EMR.

Volumes of user-generated contents have caused the problem of information overload and hindered Internet users from browsing and retrieving information. Social tagging that allows users to annotate resources with free preferred keywords to ease the access to their collecting resources. Though social tagging benefits users managing their resources, it always suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag because tags are freely and voluntarily assigned by users. Tag recommender systems, which follow some criteria to select from the tag space the most relevant tags to the user’s annotating resource, drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. This study takes personalized tag recommendation as an incremental clustering problem and proposes a Progressive Expansion-based Tag (PET) recommendation technique. The incremental clustering assumes each object appears in sequence and then is incrementally clustered into either an appropriate existing category or a created new category. The PET technique can classify each resource into multiple categories (i.e., tags) or label it as new. While a resource is labelled as new, it will recommend a set of tags that have been used by other users and are relevant to the target user’s practices. Finally, our empirical evaluation results suggest that the proposed PET technique outperforms the traditional popularity-based tag recommendation methods, while the performance rates achieved by both techniques are not satisfying.

Donating money to online content creators is gaining popularity in the last few years, especially in Mainland China. Different from mandatory payment mode (e.g., pay subscription fee to access online content), despite some user-generated content (UGC) is free and openly available, audiences are allowed to voluntarily donate money to content creators after their consumption. As a relatively new phenomenon, little is known about audiences’ donation behavior. Drawing on value-based adoption model (VAM), this study unearths perceived value as a key factor that determines audiences’ donation behavior. In addition, antecedents of perceived value are explored, including the benefits (hedonic benefit, utilitarian benefit, and social benefit) and sacrifice (perceived fee) of UGC consumption. A conceptual framework with following hypotheses is then proposed: (1) benefits of UGC consumption are positively related to perceived value, (2) sacrifice of UGC consumption is negatively related to perceived value, (3) perceived value of UGC positively influences audiences’ intention to donate money to content creators. Data will be collected to test the framework and verify the hypotheses in the future. Donating for UGC is an emerging topic in new media era, and this study represents the first step toward explaining voluntary donation behavior in UGC consumption.

Social media facilitates information dissemination and narrows the distance between merchants and consumers. However, how to better integrate social media and how to communicate promotional messages to consumers have not been fully understood by merchants. Anchoring on the concept of psychological distance, we argue that the effectiveness of social media communication depends on the degree to which consumers perceive the psychological distance of the deal communication. The social media communication data of the group-buying website were obtained from its national and city accounts. Data on the featured deals in these two cities were collected, and the sales performance of these deals was used as the indicator of the effectiveness of promotional message communication. The results indicate that promotional messages conducted on the national account induce better sales performance.

To increase user engagement is an important goal and major business model for many web applications and online publishers. An established tool for this purpose is online polling, where user opinions, preferences, attitudes and possibly personal information are collected to help publishers to a better understanding of their target audiences. These polls are often provided as supplements to online newspaper articles, the topics of which are typically also reflected in the content of the polls. We analyzed and categorized this content, and related it with the user engagement rate given as the proportion of people who voluntarily disclose personal information. Recently, public privacy awareness has increased, especially since the introduction of the European Union’s General Data Protection Regulation (GDPR). Extensive media coverage has led to public discussions about data protection and privacy. This study additionally investigated the effect of increased public privacy awareness on individual privacy awareness and subsequently user engagement. The results are based on live data of more than 60,000 polls and more than 22 million user votes, mainly collected in German-speaking countries, and give insights into user behavior when confronted with requests for personal information in various settings and over time.

In the age of social media, after a crisis event, firms will immediately take responsive actions and publicly announce what they do in news media, in order to attenuate the potential negative impact of crisis. Afterwards, the public in social media will freely discuss and deliver their opinions in the form of word-of-mouths (WOMs), which directly reflect their feeling of firms’ responsive strategy. Therefore, based on big data analysis in social media, we attempt to establish the link between crisis firms’ responsive actions and public perceptions reflected in online WOMs. Large quantities of secondary panel data are crawled from both search engine and social media. To obtain a robust result, we adopt Panel Vector Auto Regression model to conduct our data analysis. The results show that increased responsive strategies of crisis firms will lead to a significant increasement in the strength of online WOMs, while a more positive responsive strategy can significantly decrease the strength of online WOMs in contrary. Our research provides profound theoretical and practical contributions.

As part of new emerging eCommerce innovations, live streaming has started to gain lots of attention in business world because of its potential capability to boost sales online. Enabling interactions among a real-time seller, users (i.e., viewers) and peer users in e-Commerce platforms, live streaming is promising to facilitate real-time interactions among seller, users and peers online, which are likely to alleviate the physical separation between sellers, users and products in cyber space. Although some businesses are proactive to invest on this new living stream platform with a goal to more effectively engage their users, it is still largely unknown whether this effort can ultimately increase their consumer conversation rates. Accordingly, this research aims to gain more in-depth insights into users’ acceptance of live streaming shopping. Based on multimedia learning and information foraging theories, this research conceptualizes user engagement mechanisms (i.e., product interactivity, communication immediacy, and peer cues) associated with a live streaming platform and furthermore explores how these mechanisms are likely to improve users’ product evaluation and their serendipity to explore more unexpected products, and in turn how they impact users’ attitude and intention to buy products on a live streaming platform. Through an online survey study with 200 users on a live streaming platform, this study finds that the identified three user engagement mechanisms significantly improve users’ capability to evaluate products and their serendipity behavior online, which also have a positive impact on users’ attitude and intention to shop on a live steaming platform.

With the thriving development of internet industry and continuous increase of consumer demand in outbound tourism in China, opinion leaders in virtual community have significant effects on consumers’ decision making process. Hence, identifying the opinion leader in virtual travel community (VTC) is significantly important for virtual community of outbound tourism. This study proposes the opinion leader recognition model based on social network analysis (SNA), and identifies leader value presented by construal influence, content influence, and activity to measure and evaluate the effect of opinion leader on consumers’ cognition. Based on the empirical data, this study examines the soundness of SNA in VTC dimensionally and comprehensively, and establishes the opinion leader recognition, which can be used in future research to explore the assessment mechanism of opinion leader’ effect on consumers.

As the microloan becomes popular with the advance of information technology, the assessment of credit risk and understanding borrowers become crucial. However, without antithetical individuals, the prediction of loan probability and risk control cannot be so accurate. As an important consumption group in current commercial society, college students usually don’t have enough disposable income thus very likely become active lenders in microloan platforms. For microloan platforms, a vital question is how to distinguish the individuals who use the loans for the right purposes. In this study, we combine the student loan data from a microloan platform with student daily consumption data recorded by a campus e-card system to examine the change of consumption behavior for those students who borrowed money from the microloan platform. Our study finds that students with loan do have distinct consumption patterns for both long and short terms. Furthermore, by applying a difference-in-difference regression method, we find significant increases in both consumption frequency and money after students borrow money from the microloan platform for both long and short terms. Our research enriches the literature on microloan in the context of college students as consumers.

Tax filing is a mandatory part a company’s tax compliance every year. This research looks at how the competence of the tax preparing professional and the tax forms are perceived by two different Small and Medium sized firm (SME) groups: one from a developed country (US) and one from a developing country (China). The competence of the tax preparing professional and the tax forms perception by the firms impacts a firm’s future intention to use the tax service provider again. Drawing upon skill assessment literature and expectation-confirmation theory, this study presents an attitude change model of a firm’s tax preparation organization in two countries: SMEs in the US and SMEs in China. In particular, this study focuses on how perceptions of competence in the tax professional influences satisfaction and attitude change related to the tax services received by a firm.

Humans are poor at detecting deception under the best conditions. The need for having a decision support system that can be a baseline for data-driven decision making is obvious. Such a system is not biased like humans are, and these often subconscious human biases can impair people’s judgment. A system for helping people at border security (CBP) is the AVATAR. The AVATAR, an Embodied Conversational agent (ECA), is implemented as a self-service kiosk. Our research uses this AVATAR as the baseline and we plan to augment the automated credibility assessment task that the AVATAR performs using a Humanoid robot. We will be taking advantage of humanoid robots’ capability of realistic dialogue and nonverbal gesturing. We are also capturing data from various sensors like microphones, cameras and an eye tracker that will help in model building and testing for the task of deception detection. We plan to carry out an experiment where we compare the results of an interview with the AVATAR and an interview with a humanoid robot. Such a comparative analysis has never been done before, hence we are very eager to conduct such a social experiment.This research paper deals with the design and implementation plan for such an experiment. We also want to highlight what the considerations are while designing such a social experiment. It will help us understand how people perceive robot agent interactions in contrast to the more traditional ECA agents on screen. For example, does the physical presence of a robot encourage greater perceptions of likability, expertise, or dominance? Moreover, this research will address the question on which interaction model (ECA or robot) elicits the most diagnostic cues to detecting deception. This study may also prove very useful to researchers and organizations that want to use robots in increasing social roles and need to understand its societal and personal implications.

The use of mobile technologies potentially alters the boundaries between professional and private spheres. Some studies report the negative overflow of work on private life, but also the possibility of a better work-life balance (WLB). As the results of previous studies are not homogenous, aiming some authors to speak about “double-edge sword” for ICT use for professional purpose after hours, workers’ attitudes were considered as mediator variables. The main hypothesis was that ICT use after hours and segmentation preference are sequential mediators in the relation between job demands and WLB satisfaction. The on-line survey has been completed by 142 workers from various sectors. The results showed that job demands, ICT use and segmentation preference explained WLB satisfaction for 30% of the variance. In more details, high job demands contributed to more ICT use for professional purposes after hours, which contributed to weak segmentation preference, this weak segmentation preference increasing WLB satisfaction. In other words, integration preference, explained itself by ICT use after hours, contributed to WLB satisfaction. The results, highlighting the determinant role of boundary management attitudes, are an innovative contribution in the HCI and WLB research: it allows to better understand the “double-edged sword” phenomena by supplying some first conditions under which ICT use turns negative issues into positive ones.

Cyber attackers gain access into systems, networks and cyberinfrastructure by escalating privileges to confidential information regardless of the efforts systems engineers put into security. The chess game between cyber offense and defense destabilizes the ability of organizations to protect their information assets. This research employs the lens of Activity Theory to study the interaction through the contradictions embedded between the cyber attackers and cyber defenders. These types of contradictions were forcefully created and simulated in the cyber security virtual lab at Florida State University for the purpose of facilitating real-world scenario-based learning experiences. Both network traffic data and interviews were collected in order to identify the boundary objects that intersect the two activity systems. Natural language processing (NLP) was adopted to explore and extract topics frequently used by both activity systems. Consciousness of cyber defense was expanded by creating contradictions, and boundary objects were identified by comparing the interactions between these two activity systems.

A new method for discussing the relationship between CO2 emissions and bilateral FDI is proposed using grey systems theory. CO2 emissions and bilateral FDI, GDP are separately regarded as the input to, and output of, a grey system to establish a grey multivariable Verhulst model, GVM(1,N). To improve the prediction accuracy, the residual modification model are combined to the original GVM(1,N) model. Based on data relating to CO2 emissions and bilateral FDI, GDP in China from 2001 to 2014, empirical research shows that the bilateral FDI help reduce CO2 emissions, whereas the GDP results in CO2 emissions.

This integrative review gathers data from published articles and user feedback for a meta-analysis of the common problems, use contexts, and recommendations. The project supplements primary interviews with secondary data from prior studies and reports, as well as online feedback and reviews. This approach helps validate user experience findings for rarely-tested products, and it helps to confirm and identify user affordances and system pain points. Findings suggest that poor visibility of system status, lack of match between system and real-world use, and opaque help and documentation are common barriers. NFIRS software programs also do not anticipate the cultural idiosyncrasies endemic to fire services (such as apprenticeship learning) that, if addressed, could help software users better recognize, diagnose and recover from decision-making errors. Firefighters request more functionality, more help to find pertinent codes, and differentiating between nondescript codes. Recommendations for improving the quality of software programs for incident reporting in fire services include improving customization features, providing templates and content guides, and improving the glossaries of common acronyms. Help systems should address the diverse backgrounds and levels of education that comprise fire services.

In 1999–2000 and 2007, the Ministry of Education, Taiwan, respectively proposed to establish a national Taiwanese opera troupe in the hope of preserving this local art. A national troupe means that the government shows its will to preserve the art, and officially recognizes Taiwanese opera as a “national art.” Yet, to our greater surprise, many professionals and artists disagreed on this proposal, for they believed once a national opera troupe was established, the domestic troupes may face the crisis of survival. Competing with a national troupe with more financial and technical support from the government, local and non-governmental troupes by all means would find it hard to survive. In other words, a national Taiwanese opera troupe could be a threat to the existence of the art itself. How would a cultural policy with good intentions turn out to be a controversy and causing conflicts among all? This situation will be explored in this paper: firstly, the government’s role will be discussed when it comes to the development of Taiwanese opera. Then, the current condition of the local Taiwanese opera performing industry will be introduced and the relevant cultural policies will also be discussed. The last part will focus on the controversy over the necessity of establishing a national traditional troupe, and the significance of having a national troupe in Taiwan. The aim is to examine the role of the government and discuss the necessity of the political actions when it comes to preserving the local arts.

Top-level staff prefers to live in urban areas with perfect social infrastructure. This is a common problem for excellent companies (“hidden champions”) in rural areas: even if they can provide the services qualified applicants appreciate for daily living, they fail to attract them because important facts are not presented sufficiently in social media or on the corporate website. This is especially true for applicants with families. The contribution of this paper is four-fold: we provide an overview of the current state of online recruiting activities of hidden champions (1). Based on this corpus, we describe the applicant service gap for company information in rural communes (2). A study on user experience (UX) identifies the applicants’ wishes and needs, focusing on a family-oriented information system on living conditions in rural areas (3). Finally, we present the results of an online survey on the value of such information systems with more than 200 participants (4).

One of the core missions of the Department of Energy (DOE) is to move beyond current high performance computing (HPC) capabilities toward a capable exascale computing ecosystem that accelerates scientific discovery and addresses critical challenges in energy and national security. The very nature of this mission has drawn a wide range of talented and successful scientists to work together in new ways to push beyond the status-quo toward this goal. For many scientists, their past success was achieved through efficient and agile collaboration within small trusted teams that rapidly innovate, prototype, and deliver. Thus, a key challenge for the ECP (Exascale Computing Project) is to scale this efficiency and innovation from small teams to aggregate teams of teams. While scaling agile collaboration from small teams to teams of teams may seem like a trivial transition, the path to exascale introduces significant uncertainty in HPC scientific software development for future modeling and simulation, and can cause unforeseen disruptions or inefficiencies that impede organizational productivity and innovation critical to achieving an integrated exascale vision. This paper identifies key challenges in scaling to a team of teams approach and recommends strategies for addressing them. The scientific community will take away lessons learned and recommended best practices from examples for enhancing productivity and innovation at scale for immediate use in modeling and simulation software engineering projects and programs.

Cross-sector collaborations are a noteworthy addition to the tools of public administration and authorities seek to collaborate in order to deal with cross-sector wicked problems. However, the rate of success is not noticeable due to the barriers to reach collaboration. This study identifies the barriers of different phases of cross-sector collaboration. A synergy map is used to classify the barriers in three clusters to address them via conceptual frameworks, visual thinking and online collaboration platforms to facilitate collaboration.

Human-Centered interaction (HCI) has played a significant role in different areas of, software engineering, robotics, programming, facial recognition, and IT systems. To date, Project Portfolio Management is a field that HCI has not yet received much attention. However, the project management field and more specifically, software of Portfolio Management has been encouraged in the practise of HCI and design thinking concepts to improve outcomes of projects. This study aims to develop a framework in portfolio management that improves the understanding of HCI in portfolio management and assist’s software developers to better engage design thinking in their approach. Furthermore, it develops a framework that can rectify issues identified from within the Organisational State Transition Approach of Portfolio Management, such as, unclear actions and strategic goals, uncertainty of success measures and key people being unsure of their purpose and what they were trying to achieve. We have applied a PACT framework as well as incorporating project management process groups to assist organizations to achieve their strategic goals. The outcome of this study assists portfolio managers to explore the requirements of their organization and enhance the success rate of their IT projects.

The crowdsourcing phenomenon offers the opportunity to address an open call to the crowd. Crowd workers may work together to find a solution that satisfies the open call. One of the major benefits for a crowdsourcer is the pool of crowd workers that can be accessed over crowdsourcing platforms. However, the produced outcomes of crowd workers are often on a low level with weak elaboration and quality. The key to high quality work is the collaboration of crowd workers. This has already been addressed in the collaboration process design framework for crowdsourcing (CPDF). At this point we position this work and widen our view by conducting a content analysis on crowdsourcing platforms in order to understand the collaboration of crowd workers on real world crowdsourcing platforms better as well as investigate the weaknesses of the collaboration process design framework for crowdsourcing to improve work practices. By doing so, we redesign the CPDF based on the results of the content analysis and present an improved collaboration process of crowd workers within the CPDF.

Science parks are important industrial clusters in the development of Taiwan’s technology industry. Nearly 280,000 employees commute to the science parks on a daily basis. Thus, traffic congestion not only wastes the time of and creates extra fuel costs for the road users, but also leads the vehicles to release more pollutants in the environment. With the rise of Internet of Things technology, the science park administration has established multiple IoT-enabled systems since 2017, in order to collect data and monitor traffic flow and air quality in a more accurate manner.However, it is still a question that how emerging technology should be applied to provide accurate and timely information to assist administration to observe the historical trends and current status of traffic congestion and air quality, so as to formulate traffic control and air pollution prevention strategies. To that end, there are two purposes in this paper: (1) to establish a Science Park Smart Governance Platform to collect data collected from the IoT devices, and (2) to design and develop data visualization functions for the smart management of traffic and air quality.The research garners three results from the smart traffic monitoring service: (1) helping administration check the traffic status in real time, in order to facilitate traffic control; (2) presenting the historical trends of traffic flow on a typical day, month, and year, and allowing administration to understand in what intersections and at what periods traffic congestion is more prone to take place; (3) creating a predictive model of how traffic flow and weather can influence the traffic volume interactively to predict traffic flow for every intersection in the following 10 min, so that administration can operate the traffic lights in order to reduce traffic congestion.Besides the aforementioned results, three other results from the smart air quality monitoring service are presented in the study: (1) allowing administration to monitor real-time air quality status in various areas of the science parks; (2) presenting a historical trend of air quality, and allowing administration to understand in what month/time air pollution is occurring; (3) when the concentration of certain air pollutant exceeds a particular threshold, the smart environmental monitoring Chabot service will push warning messages to the managers.

National laboratories can promote the construction of academic teams, improve the level of scientific and technological innovation, improve the quality of personnel training, improve academic material conditions, and create a strong academic atmosphere. It is an important support for creating world-class disciplines. Visual elements are one of the main components of the laboratory environment, and strengthening the construction of visual management is of great significance. However, visual management laboratories are not perfect in most countries, and even some laboratories have not yet been established, lacking in structural layout, laboratory equipment display, lighting, walls, ground color, signs, slogans, guiding laboratory settings, etc. The visual programming of the system also brings up some new problems, mainly reflected in the efficiency and safety of the human, equipment, environmental, psychological and emotional experience of the experimenter. Therefore, how to strengthen the visual management of national laboratories has become a new topic in the construction of national laboratories.