HCI in Business, Government, and Organizations

While creating an optimal assortment of products, assortment planners need to take into account an important amount of information, which leads to a certain level of uncertainty. These trade-offs can diminish the quality of the assortment decisions made by the planners. To reduce their impact, assortment planners can now use artificial intelligence (AI) based recommendation agents (RAs) throughout their decision-making process, thus benefiting from their ability to process a large quantity of information to improve their decisions. However, research on user-RA shows that there are some challenges to their adoption. For instance, RA adoption depends on the users perceived credibility of its recommendations. Hence, this study investigates how the richness of the information provided by the RA and the necessary effort to access this information influences the assortment planners’ usage behavior (visual attention) and perceptions (credibility, satisfaction, performance, intention to adopt the RA). A within-subject lab experiment was conducted with twenty participants. The results show the importance of the RA’s recommendations that include easily accessible explanations of the variables included in their calculations on the usage behavior, perceptions, and decision quality of the assortment planners. These findings contribute to the HCI literature and the theory of RA adoption in B2B contexts by providing insights on features enhancing employee adoption.

A significant challenge to maintaining security within an organization is the training of a non-technical workforce to respond appropriately to cybersecurity threats. This work describes an online environment that utilizes experiential learning to give non-technical workers an increased exposure to issues in cybersecurity. We present a simulation-based approach that provides a better understanding of specific cybersecurity threats through experiential learning. The presented interface uses simulations of cybersecurity threats to provide concrete experiences rather than descriptions. While moving through the simulation, the user can attempt multiple actions and is provided with an “awareness” measure. For each, the system provides continuous feedback to allow active experimentation. After each threat has been exposed, the environment provides a narrative of the user’s actions with suggested improvements to allow for reflective observation. This work includes a user study of the interface, shows the results of usability testing, and evaluates the effectiveness of the training through simulation.

Knowledge base question answering (KBQA) can be decomposed into entity linking and relation extraction. In KBQA relation extraction, the goal is to find the appropriate relation given the question and its linked entity. Previous work used neural network models to process entities in a pairwise manner, which is well-suited to large relation sets in KBQA. However, such models must execute the same relation detection procedure multiple times for each question to complete an exhaustive search of the relation combinations. In this paper, we propose treating relation extraction in KBQA as a classification problem. Moreover, we introduce a masking layer which filters out less probable relations in advance. Experiments show that the masking mechanism benefits the proposed model by improving the accuracy from 72% to 77%. In addition, a catering knowledge base is constructed automatically in this paper, on which the proposed model yields an accuracy of 89%, demonstrating its effectiveness.

Risk abounds as individuals engage in activities involving the sharing of sensitive information through a third-party cloud storage tool. In this research, we investigate the storage and sharing of very sensitive information (an individual’s tax filing information) through a specific third-party technology provider, Google Drive. Within the specifics of such a potentially risky act we argue that information assurance mechanisms implemented by the cloud storage service provider reduce risk perceptions of individuals even when very sensitive information is being shared. Previous positive experience with a cloud service is likely to mitigate concerns on information sharing on the cloud. To elaborate this proposed relationship a research model of information assurance is proposed and tested in the context of tax filing sharing intention.

It is widely held that leadership consists of behaviors that should be applied strategically and systematically to motivate individuals and teams to perform. It is also self-evident that we live in a world of automation, artificial intelligence, and expert systems. Given these two assertions, we propose that some aspects of leadership are candidates for automation. This paper briefly reviews relevant leadership literature and describes three leadership behaviors that may be possibly automated: goal setting, performance monitoring, and performance consequences. The paper also explores the relationship of different embodiments of the artificial leaders and the impact of these embodiments as varying degrees of social presence and the effect of this presence on performance and satisfaction outcomes.

The latest generation of head-mounted displays such as HoloLens provide mixed reality capabilities that claim to better integrate the real and virtual worlds. In this paper, we would like the share our experiences in implementing a user interface for an assembly assistance system using the HoloLens. We carried out a preliminary evaluation of the applicability of mixed reality using the perspective of developers and expert users in an assembly scenario that allows us to operate and compare two interfaces - a state-of-the-art projector display system and the HoloLens. We believe our findings may contribute towards a better understanding of the effects of new display technologies such as the HoloLens in developing and using assistance systems in other fields as well. Areas that may be of future research are also highlighted.

In the emerging era of Industry 4.0, many factories have launched a series of initiatives to enhance their lean production by adopting the information technology and the business analytics as the tool of business transformation [1]. Among these initiatives, continuously improving the quality assurance is the compelling driver and the cornerstone of rival differentiation [2]. But the satisfaction of a product significantly depends on its components’ quality which were procured from the different suppliers at different times; thus, to increase the customer’s confidence in using the product, the more effective collaboration in the supply chain plays the key role to success. The material traceability system is a part of the supply chain information visibility [3]; not only putting the accountability in place, but also disclosing the whole journey of each lot of production. This paper revisited the current collaboration model and to explore where went wrong in the past, proposes the feasible traceability requirement taking framework, and presents the appropriate data model for such a system.

The development of mobile application software has grown explosively in conjunction with the worldwide use of smartphones in recent years. Since new forms of cyber crime and fraud are also emerging, Caller ID apps are also becoming more popular in recent years. Despite abundant research on users’ behavior intention for mobile app usage, the influential factors in the information sharing intention of apps have never been significantly explored to date. In this study, theoretical foundation is built based upon the IS success model, and empirical data obtained from 230 users who had experience with Caller ID app were discussed by using PLS-SEM as an analytical tool. The results indicated that information quality, technical quality and service quality are the main factors that affect users’ trust and satisfaction. The findings of this study provide several vital implications for researchers to further examine phone-call-related applications, especially Caller ID apps.

For companies that want to be successful in China, it is necessary to understand the needs and behaviors of Chinese consumers. However, the UX community in the US has yet to develop the best practices for conducting remote user research in China. Existing guidance seldom discuss the feasibility of the research plans, such as cost or time constraint. As a small User Experience design team with limited resources, we strived for conducting user research on Chinese users in a cost-effective way. This paper summarized our experience of exploring research methods and tools for studying printer users in China from the US, and provided advice to the UX community that faces similar challenges.

The challenge of big data era to the operation and management of financial products boil down to how to turn data operation capacity into the core competitiveness. Under the existing operation and management model of financial products, the unity between the efficiency and benefits of operating activities can be hardly achieved in big data era. Kansei engineering technology is targeted at better coordinating the relationship between consumers and products and providing a solution to the contradiction between the width and precision of big data information while conveying the usage requirements of products. This paper takes the lead in applying the Kansei engineering thoughts in non-industrial fields to the optimization of financial products operation. It proposes the concept of Kansei operation by analyzing the product lifecycle management of financial products, which is of prime importance to correctly process the structured and semi-structured Kansei information, reduce data mixture and achieve operational excellence. With the differential customer perceptual analysis as the research basis, this concept applies the conversion of sentimental demand quantification into real values to the optimization of financial products operation, thus putting into use the operational excellence in the big data era.

This paper explores the convergent validity of instruments that can provide higher temporal resolution when measuring user experience: the continuous self-perceived measurement system and psychophysiological measures. Specifically, we explore the extent to which primacy and recency effects may have an impact on the convergent validity of two constructs: valence and arousal. Using a Wilcoxon signed rank test, results suggest that users self-evaluate their valence more accurately at the end of each of the sequences than at the beginning while they evaluate their arousal more accurately at the beginning of each of the sequences. This suggests that the recency effect has more impact on valence and the primacy effect has more impact on arousal. These findings contribute to human-computer interaction research by providing more information about the psychophysiological measures that cause recency and primacy.

Knowledge work often requires people to read and comprehend documents in order to fulfill their tasks. To support knowledge workers in their real working environment semantically enriched texts can be leveraged. One technical basis is Named Entity Linking (NEL), which provides the capabilities to identify entities in a text and link them to a knowledge base that provides further information about them. This provides several opportunities to improve the outcome (e.g. text comprehension). In this paper, we lay the foundations for evaluating such semantic text enrichment environments that can be used in different business cases. The main result is an approach for measuring the effects of semantically enriched texts in the working environment comprising the five dimensions text, enrichment, reader, activity, and output.

Digitalization fundamentally changes the way business is conducted, since economies of scale and scope are delivered at an unbridled speed in the digital age. These circumstances induce a quest for ambidexterity among the IT department: the contemporary IT function is expected to deliver reliable and stable services as well as rapid innovation - at the same time. The two-speed IT concept aims at harmonizing these vastly different requirements by decomposing the IT function into a traditional and an agile mode. While benefits of the concept are widely acknowledged in theory, little is known about its real-world application. Our study captures notions from a practitioners. Specifically, we want to elaborate how practitioners evaluate the concept against the backdrop of the challenges induced by digitalization. By conducting semi-structured interviews with people responsible for IT, we were able to gather rich insights on the perceived benefits and perils of bimodal IT setups and highlight important topics for future academic research.

The time delay between a user’s initiation of a command on a digital device (e.g., desktop computer, tablet, smartphone) and the system’s task completion, including the display of the result on the screen, is referred to as system response time (SRT). This specific system property has been the object of study since the 1960s, predominantly in the field of human-computer interaction. In most usage scenarios, SRT ranges from milliseconds to several minutes, and SRT is a function of various factors, including technical system capabilities such as processing power. One would assume that technological progress has reduced the relevance of investigations into the physiological and stress-inducing effects of long and/or variable SRT. However, as a result of the ever increasing complexity of information systems and digital devices, SRT is still a significant stressor in today’s society. One could even argue that, due to the ubiquity of digital devices in almost every corner of life and the resulting frequent human-computer interactions, the relevance of SRT as a topic in scientific research and practice has even increased in the last years. Against this background, the present article conceptualizes SRT as a stressor in a digital world, reviews major research results, and, based on that review, develops a theoretical model. This model is intended to guide future research on SRT.

This study is set out to examine the determinants that drive preventive/protective as well as abusive behaviors among employees in the context of information security by extending the health belief model - a model set out to explain and predict healthy behaviors in human beings. A field experiment, accompanied by online surveys in two financial organizations in the US and India is conducted, measuring employees’ actual security behaviors. We identified factors (perceived susceptibility, perceived barriers, and self-efficacy) that have the largest effect on employee’s security behaviors. We offer several theoretical contributions and implications for practice.

This research-in-progress paper presents a conceptual system for automated deception detection in online interviewing. The design proposes video recordings of responses to predefined, structured interview question sets variously selected based on the desired behavioral metric of interest, such as competence, social skills, or in this case, veracity. Raw behavioral data extracted from video responses is refined to produce indicators of behavioral metrics. A prototype implementation of the design was built and tested experimentally using a job interview scenario. Results of the experimental analysis provide evidence of the potential of the concept.

This study examines the current application of 360-degree VR videos in the news industry. Both the advantages and challenges of 360-degree VR videos in enhancing audience experiences and engagement are discussed. To better understand the effects of immersive technology on audience engagement, this study selects the case of The New York Times (NYT). Data were crawled from 598 videos on the NYT YouTube account for analyses. The results showed that 360-degree VR videos generally performed worse than non-VR videos in enhancing audience engagement. An interaction effect was found between video format (360-degree VR or non-VR) and content genres.

A service system is generally very complex that it includes several views, such as data, function, structure, behavior and so on. There are two different ways to model such many views. The model multiplicity approach for service systems design separately chooses a distinct model for every view. On the contrary, the model singularity approach for service systems design, instead of choosing many isolated models, will use only one single integrated model. In this paper, we proposed a structure-behavior coalescence (SBC) systems modeling approach for service systems design which is based on model singularity. The multiple models are un-related and therefore inconsistent with each other, which become the primary reason for the model multiplicity problems. Being able to think about a system in one single integrated model, the SBC systems modeling approach for service systems design truly avoids the model multiplicity problems.

Data breach is a serious global security and trust concern. Data breaches have both direct and indirect as well as a short-term and long-term financial implications for the victim organization. One of the significant long-term financial costs is the loss of consumer trust in the organization. It seems that the sheer number and size of data breaches, insider data breaches, in particular, is not about to slow down anytime soon. There is a need to heighten information security to prevent such breaches, and there is a need to deploy trust-building strategies to minimize the trust fallout from such breaches. There is plenty of evidence that consumers use corporate social responsibility (CSR) as a means of differentiating one company from another. In this paper, we examine if CSR strategies such as embracing a social cause (LGBT and nature) could assist in repairing organizational trust in the wake of a data breach.

In order to study the cognitive process of brand recognition in different positions (left, middle and right) of the web navigation bar, this paper adopts the Oddball experimental paradigm in event-related potentials combined with behavioral data for experimental investigation. An analytical comparison of the P300 amplitude of the LOGO located in three certain positions in the navigation bar is conducted. The LOGO placed on the left/right can generate larger amplitude than the LOGO placed on the middle. The experimental result shows that LOGO location in navigation bar have a great significance on brand recognition. From neural mechanism of visual cognition processing, ERP can objectively and effectively obtain the implicit feedback of web brand recognition from users, which provides a quantitative index for establishing an accurate evaluation model of web brand recognition.

The design of a system interface can impact user judgements among expert and novice users alike. With information systems, fundamental design choices can either augment or distract individuals in identifying patterns of converging data points. The goal of this effort was to compare the influence of Likert and categorical type rating scales in a system used to guide analysts through a content analysis process. While these scales have been examined in the context of psychological assessment literature, little has been said about their impact on decision makers from a human computer interaction perspective. We conducted a laboratory experiment to explore the effect of using Likert and categorical scales in an intelligence assessment task using unstructured data. The dependent variables included (1) Likert versus categorical type scales and (2) analyst experience (novice versus expert). Results indicated that expert and novices both had greater confidence and more creative, accurate responses in the interface utilizing Likert decision scaling.

We investigated the role of initial trust in an e-commerce context (hereafter e-Trust) that individuals perceive based on the information that is presented on the landing page of an app in an app store. In order to establish external validity, we collected data based on a set of realistic sport tracking apps and conducted an online experiment. Based on a sample of 2,042 individuals, we found that as in other online contexts (e.g., online shopping), e-Trust plays an important role in the context of mobile app transactions, significantly influencing an individual’s decision to download or purchase an unfamiliar app. Interestingly though, we found that e-Trust has a different factor structure in this specific context compared to previous studies. In addition, not only individual disposition to trust was an important antecedent of e-Trust, but also gender, though in a different way than we expected. Men reported lower levels of e-Trust than women.

Usability, functionality and sociability have been found as the important factor in social commerce design. However, it is not clear about the influence of social commerce design to users shopping behavior, which needs to be further explored. It is arguable that such lacks of understanding may cause ineffective social commerce design and hamper users’ online shopping behavior. Therefore, this study aims to explore the impact of social commerce design on user shopping decision-making. We believe that the results can provide a useful foundation for future social commerce research.

Consumers are becoming dependent on online product reviews for making purchasing decisions. Although reviews are available directly on e-commerce websites. For better quality reviews consumers are using extensive resources like Google and Amazon. Social media could be a great resource for looking up product reviews as people post about their latest purchases on social media. However, it is hard to lookup reviews on social media and consolidate them. For in-store purchasing looking up reviews becomes challenging as there are very few reviews on in-store products. Consumers need to visit several websites while standing in front of the product to get reviews and consolidate all the information themselves to make a decision. Our proposed app will scan the product packaging, lookup reviews over different social media platforms, consolidate it and display reviews in augmented reality. Our results show that social media reviews are helpful in making buying decisions. Although augmented reality didn’t make a big difference in improving the usability of the app, consumers still showed a positive inclination towards it.

This research examines attention to distracting products unrelated to the shopping goal and its impact on performance when making online decisions. An experiment was conducted with thirty-eight participants in a laboratory setting. The study used a 2 (product similarity: similar vs. non-similar) × 2 (visual proximity: near vs. far) within-subject design. The attention of participants was measured with eye tracking during an online decision task. The results showed a significant effect of distractors’ visual proximity as participants spent more time on products that were near the target stimulus. In addition, the analysis yielded an interaction between product similarity and visual distance on users’ attention. Finally, distractors that were similar to the focal stimulus positively influenced decisions accuracy. These findings contribute to theory by providing quantitative measures of the Gestalt law of proximity. In addition, the user experience has become a cornerstone for the success of firms and the conclusions have HCI design implications about effective product presentations in online shops.

Everyone has experienced emotions and everybody knows what they are – at least, we think we know. Asked for an explanation, the response will likely be something along the lines of “happiness is an emotion” or “if I’m scared of something, it is also an emotion.” Such imprecise and in the narrower sense incorrect definitions are entirely sufficient for everyday use. But this is obviously not the case in science as well as in corporate practice. After all, important recommendations for the economy should be derived from well-founded studies. As a first step, this requires that the term “emotions” means the same to everybody. At the present time, this is wishful thinking. A steadily rising number of publications are produced within the HCI community, in which in part elaborate measuring techniques are used to account for emotions with different study designs. However, this approach appears to be problematic, because a standard definition of “emotions” has yet to be established. The purpose of this publication is to draw the attention of the HCI community to this issue and to start a worthwhile conversation with an attempt at providing a definition. It concludes with a specific recommendation as to which physiological measuring methods would be conducive for answering corporate research questions.

Many e-stores adopt personalized recommender systems to provide service for the customers nowadays, which they can rely on to predict customers’ preferences based on the detailed individual customer information. Customers got better services provided by the personalized recommender systems. However, customers also concerned that the websites may steal, misuse or sell their information to a third party. Such situation causes the “personalization-privacy paradox”. This study proposed a research model based on the privacy calculus theory to explore how the customers make decision between personalized service and privacy concern. An online survey was conducted to collect empirical data in order to test our research model. The results of PLS analysis indicate that personalized service is positively affects perceived benefit. Both information sensitivity and privacy concern positively affects perceived risk. However, when customers with low information sensitivity and low privacy concern, they are less likely to evaluate associated risks. Perceived value is influenced by perceived benefit and perceived risk and in term, affects customers’ willingness to provide personal information. The findings of this study provide implications for both researchers and practitioners of using personalized recommender systems.

Consumers regularly make decisions. Some of these decisions are relatively simple, such as a selecting a jam or a coffee, where the choice is entirely subjective. Others, such as investment decision-making, are risky, complex, consequential and there is a normatively optimal choice. Seeking advice from an expert is a reasonable solution in these circumstances and yet a minority of investors turn to a professional for advice. As an alternative to human advisors, technology is increasingly being harnessed to provide effective and low-cost advice to assist consumers in making decisions. In a retail context, these are shop bots and search engines often used on a mobile phone while shopping. In an investment context, these are frequently referred to as “robo-advisors”. Examining consumer intention to seek advice in an investment context, the current study demonstrates that, among numerous factors examined, unfounded confidence was the best indicator of consumer reluctance to seek advice. Robo-advisors, as artificial intelligence agents providing financial literacy instruction and impartial expert advice, may offer a solution.

This paper explores the factors that predict a user’ success at installing a home electronic device. Specifically, the objective of this paper is to investigate the effect of a range of attitudinal, psychophysiological, and behavioral factors on the success of an electronic equipment setup. Building upon an experiment conducted with 29 participants, two factors appears to have an impact on successful equipment setup: (i) when the user remains calm during the installation and (ii) when the user takes the time to read the instructions. These findings contribute to human-computer interaction research by highlighting the importance pre-experience stages such as unboxing and installation as they may impact the overall experience of use.

Bank and trust – two words but one meaning in customers’ minds. When interacting with financial service providers, customers are consistently looking for “trust signals” that comfort their decisions and “distrust signals” which create doubt. Therefore, service providers need a deep understanding of the customers’ requirements and wishes. To identify trust and distrust signals, we combine established user experience research methods with a new testing procedure to gain helpful recommendations for optimizing the online appearance of banks. The contribution is divided into three parts: Firstly, we investigate current approaches in the financial service industry. Secondly, we provide a corpus describing the relationship between the customers’ perception of a bank’s website and trust. Thirdly, an empirical study based on qualitative user experience testing with banking website customers shows the value gained by optimizing the banks’ virtual interface by enhancing “trust signals” and avoiding “distrust signals”.

With the increased digitization of businesses and consumers, it is now not unusual for consumer businesses to solely engage with their customers through digital channels. Many such app driven businesses adopt a freemium business model, whereby use of the entry level service is free, whilst provision of premium content and services is for a fee. With no person to person contact with their customers, these businesses must rely on consumer data analytics to guide decisions on consumer marketing to incentivize users to adopt the premium services. This business scenario provides the motivation for this research project. In our research, using machine learning classification techniques, the team identified the key customer app usage attributes which were the main predictors of future paying subscribers.

With the intensified competition, attracting and retaining the customers has become a top concern for businesses. Color has been identified as one of most significant factors influencing consumers’ purchasing behavior. The purpose of the research is to explore the ways color perceptions influence consumer behavior. A better understanding of influence of color perceptions enables them to direct marketing strategies design and implementation. In the research, the researcher makes an attempt to review relevant research papers and account for why color is important in capturing the interest of consumers. In the paper, consumer behaviors, influential factors and influence of colors on consumer behavior, color meanings and roles are reviewed and critically discussed. It is expected that the study can help better understand consumers’ color perceptions and their potential influence on consumer behavior. The color psychology has become growingly significant in producing and packaging products. Colors, thus, accomplished prominence in marketing activities. The findings in the established literature can offer some great implications for businesses in terms of how to leverage color for creating distinct customer experience. The established literature also highlights colors have different meanings in different culture. That indicates the importance of considering cultural difference when exploring influence of color perceptions on consumer behaviors. The paper simply reviews the established literature, and these established findings remain to be empirically tested.

In recent years, product reviews have taken an important role in helping consumers make online purchasing decisions, but only a small proportion of consumers post their reviews online. Researchers pointed out that consumers can be divided into distinct groups in terms of their motivations for eWOM (electronic word-of-mouth), and different strategies should be developed based on different motivation groups. However, the effort of explicitly acquiring motivations via questionnaire is unavoidably high, which may impede developing different strategies to different motivation groups. In this paper, we identified a set of consumers’ motivations and behavior data. Then, we performed a user survey to validate whether the behavioral features are significantly correlated with consumers’ motivations. These findings lay solid foundation to develop more adaptive design solutions for encouraging eWOM participation.

This study examines users’ risk-taking behavior in software downloads. We are interested in quantifying the degree of risks that users are willing to take in the cyber security context. We propose conducting an experiment using Amazon’s Mechanical Turk to assess the degree of risks that people are willing to take for monetary gains when they download software from uncertified sources.

Gamification has been increasing in popularity in a variety of online context, including online learning. However, its impact on intrinsic motivation is still unclear. In this research, we carried out an experiment to assess the impact of providing two gamification features in an online learning system – point and leaderboard – on intrinsic motivation.

Social media has been widely used for both professional and personal communications. Businesses recognize the importance of social media and are using them to fulfill various business objectives. In this paper, we focus on analyzing the business objectives of public accounting firms that have both a firm-wide main page and a career page on Facebook. More specifically, we compare the business objectives they are achieving with their firm-wide main pages versus career pages. We not only find differences in the objectives that are being achieved, but also identify other objectives that are not actively being pursued on either page but may be considered in the future.

This study is an extension of two previous studies, which explored the relationship between happiness and Internet use [1, 2]. An Internet Use Scale (IUS), developed in the initial study, was administered to college students along with the Flourishing Scale [3] and the Satisfaction with Life Scale [4]. I compared changes in the relationship between these measures, and their mean values, across the three samples; assessed the relationship between use factors and happiness over-all, combining data from all samples; and evaluated the relationship between individual usage scale items and happiness. Results indicated that those who reported spending less time on the Internet, and less time expressing negative emotions scored higher on measures of happiness. There was also some indication that those who spend time checking facts on the Internet are happier, but the effect was not as strong nor consistent.

This paper addresses the issue of social tagging, especially how the elderly give tags in digital resources. The connection between mental models and the principles of the elderly’s tagging behavior is made. The research method used were two folds. First, two rounds of revised Delphi method were used to obtain the consensus of how the elderly provides tags after viewing a 10-min YouTube clip regarding cultural heritage information. The research team conducted in-depth interviews with 20 elderly citizens who are over 55 years old and are residents in northern Taipei, Taiwan. Each participant was interviewed for about 30 min. After interview sessions, the researcher asked each participant to draw a fishbone diagram with the purpose of grouping in more detailed manner the tags and sub-tags generated from interviews. The analysis of the interview and fishbone diagrams showed that the types of tags the elderly considered fall into three categories: concrete objects, emotion aspects, personal memory and related experience. In addition, the prompts the elderly considered when giving tags are based on explicit texts, buildings, and key people shown in the film. Lastly, the factors the elderly considered when giving tags are mainly because of repeated themes, concrete objects, as well as personal and communal memory from the past experience.

The advent of the internet broadly changed the way society communicates. This is well-known and well-researched across standard populations. Niche or subpopulations, such as terrorist groups or other violent extremist organizations, are less well-understood and researched. However, as strategic outreach such as recruiting and advertising is more and more web-based, research’s limited understanding of such groups is a detriment to effective management strategies. This work is a foundational attempt to ground violent extremist organization’s internet usage into the theoretical lens of and discussion around digital participation and online communities. We analyze the spectrum of participation in violent extremist organizations’ online communities and elucidate existing behaviors of individuals involved with terrorism in a series of mini case studies. This work’s contribution is the expansion of the theoretical framework of digital participation in online violent extremist communities.

Social networking sites (SNSs) have become one of the most frequently used communication media. While many studies have examined the motivations of SNS usage and visiting behaviors, this study mainly focuses on exploring the role of trust in an SNS and intrinsic motivational factors affecting continuous usage behaviors, using the flow theory. Using a web-based survey of 291 college students, the age group that most frequently uses SNSs, our research findings suggest theoretical insights. The flow experience—consisting of perceived enjoyment, perceived control, and concentration—serves as a salient antecedent to explaining SNS users’ satisfaction, which in turn has a positive effect on continuous use of an SNS. Intent for continuous usage of an SNS mediates the relationships between satisfaction and actual use behaviors. Our research findings also reveal that trust in an SNS significantly affects all other constructs, including perceptions of SNS users, intentions, and actual behaviors. Further discussion is detailed, and the limitations of this research are addressed.

Online polls are considered a valuable method of collecting users’ opinions, attitudes and preferences. They also help to increase user engagement, which is a goal of many online publishers, as they seek to understand their target audiences better and therefore want to collect and analyze user data. Gaining access to information from their users’ social network accounts is seen as a significant advance and consequently social login functionality is becoming an increasingly common feature of various web applications. Users appreciate the convenience and benefits of this, but are often unaware of the privacy issues that arise. This study investigated the influence of different types of privacy alert on users’ decisions whether to connect an online polling application to a social network, thereby granting access to their social media data in exchange for seeing their friends’ votes. The method used live data from real polls in German-speaking countries and gives insights into user behavior when confronted with requests for Facebook data. Differences in privacy awareness and user decisions between our research and previous studies in laboratory settings are addressed as well.

This research studies the micro-celebrity endorsement effectiveness. To be more specific, we consider a typical social media enabled micro-celebrity: bloggers. This research asks two questions: 1, how does bloggers leverage the social media to recommend products; 2, what are the underlying psychological mechanisms? We adopt attachment theory to explain the research questions. We propose that factors that can fulfill the follower’s self-enabling needs, self-enriching needs and self-gratifying needs are important to build attachment towards micro-celebrities. In advance, the attachment will lead to purchase behavior. A conceptual model is built and the further data collection will be conducted later.

It is a widely accepted truth there are great values embedded in the opinion and sentiment expressed by users on social media platforms. Nowadays, it is quite common for researchers or engineers to adopt opinion mining and sentiment analysis techniques to extract enriched emotional information from online text content. However, given the characteristics of social media, such as dynamic, short, informal and context dependent, applying general opinion mining and sentiment analysis techniques originally designed for static long text corpora would lead to serious bias. In many applications, even research that not specialized in opinion mining and sentiment analysis, this problem is ignored unintentionally or unintentionally. Such ignorance may contribute the failure of some designs or unexplainable results. In this paper, we summarized these challenges in social media sentiment analysis. Some potential solutions for these challenges are also discussed. Finally, we also introduced several state-of-the-art techniques in social media sentiment analysis.

Scholars have strived for more than two decades to understand and conceptualized technology affordances. While some claim that affordances should be at the core of the HCI discipline, there is limited consensus regarding how to define and operationalized this concept. In recent developments in the IS literature, perceived affordances are operationalized as the relationship between the actor’s goal and the technology’s features. In this research, we refine the concept of affordances by incorporating the new factor of ‘actor capability’ and test this claim by introducing and validating a three-way interaction between goal, capability, and feature in an open innovation context. Our contribution provides a more nuanced yet powerful way of understanding technology affordances from both theoretical and practical perspectives.

The dominant rhetoric of smart cities is mainly grounded in the large scale adoption and implementation of cutting-edge technological innovation by local governments and cities all over the world. Responsible context sensitive and social innovations are key complementary drivers to reduce digital divide within and between cities and promote dividends and human value such as dignity, inclusion, urban upgrading, participation and better user experience. This research reports on the continuous efforts performed at the Iranian Urban Development and Revitalization Corporation (UDRC), from February to August 2017, to implement social innovation for urban upgrading by redesigning it’s website as open ICT ecosystems for improved user experience. The CUBI UX model is applied and continuous remote user research is conducted for three months to measure and understand the user experience of the old website. Then, after redesigning the old version of the website into a new one, interactive prototyping and implementation, user experience is being measured during the next three months. Finally, comparative analysis of user experience measures between the old and the new version of the website are presented and discussed.

The widespread use of urban software and information technology infrastructure systems now demand new levels of complexity in data generation and data application across interoperating domains. Given this context, and discoveries in visual analytics research that reveal knowledge is created, verified, refined and shared through the interactive manipulation of the visualization (Pike et al. 2009), defining a taxonomy of visualizations can assist visualization system designers in understanding key visualization techniques that serve multiple linked user groups (Chengzhi et al. 2003). It could also be meaningful to others working in sectors that are now in the process of interoperating through the pervasive nature of digital economies. Understanding the potential components of a taxonomy for these forms of data visualization demands the identification of inter-relating and diverse user groups utilizing the same data for multiple tasks (Mahyar et al. 2015), the complexity of visualization processes, relevant task levels and interactions to supplement human insights. For example, a visualization displaying urban transit data might support the requirements of a wide array of users such as urban-designers, city-planners, data-scientists, engineers, transit-managers, pedestrians and transit users. This paper discusses the taxonomy design and prototype creation process for a user-centered taxonomy for urban transportation applications developed by the Visual Analytics Lab at OCAD University, as part of the VAL’s research and design contribution to the iCity research project, a collaboration between academic researchers, industry partners, city transportation planning departments and transit authorities that seeks to develop software support systems for transportation planning.

The expansion of Internet has eased the broadcasting of data, information, and propaganda. The availability of myriads of social and televised media have turned the spotlight on violent extremism, widened the rift between different sides of the spectrum, and expanded the scope and impact of ideology-oriented acts of violence on citizens and nations. The human casualties and psychological impacts on societies make any study on such acts worthwhile, let alone attempting to detect patterns among them. This study focuses on mining the information about each violent act, including human casualties and fatalities, level of coordination and expertise, importance of the targeted process, and the extent of its impact on the process, to identify the responsible group. Decision tree, a non-linear classifier, reached 20% cross-validation accuracy in identifying the correct group among 38 groups. This is the highest accuracy achieved in comparison with other linear classifiers, including Perceptron, SVM, and least squares. Our results also underscored the human casualties and fatalities as the most important predictors. The other four variables, including level of coordination, level of expertise, importance of the targeted process, and the extent of the impact on the process were all partly correlated and less helpful. However, the single feature, generated by linear combination of these four features using PCA, was as good of a predictor as the human casualties and fatalities.

New technology is dramatically changing the workplace by allowing companies to increase efficiency, productivity, quality, safety, and overall profitability. An effective new technology implementation is required for companies to compete successfully in the marketplace. Time and money wasted on unsuccessful and improper new technology implementation is contrary to the overall goal of improving the competitiveness and profitability of the company. Teams and teamwork have been recommended as a way to improve efficiency, productivity, quality, safety, profitability, and employee satisfaction. New technology challenges the current implementation methods and techniques. To effectively utilize these new technologies it is best to consider all the factors involved in the implementation process; most importantly the individual human elements involved. It is recommended to utilize a cooperative team oriented approach to new technology implementation, which relies heavily on obtaining employee input and participation throughout the entire process. By doing this; it is hoped that the new technology can be implemented in the most effective way possible.

In order to achieve the safety and security within a city, it is important for every citizen to understand when, where, and what kind of incident occurs frequently around oneself. Previously, many studies have been conducted to clarify relevant factors to street crimes. However, the previous studies mainly focus on the scale of city and ward, but do not consider the magnitude of town and town block. In this paper, we analyze street crimes within a Japanese city, Kobe, from micro-viewpoints rather than conventional macro-viewpoints. More specifically, we focus on the relation between population and street crimes. We use micro-level demographic data for each town block in Kobe city as population data. We also use incident data published by Hyogo Prefectural Police. Integrating these data, we conduct the fine-grained analysis of the relationship between population and street crimes. Furthermore, we develop a system that visualizes the frequency of street crime on the map. Using the system, we can see town blocks with frequently street crimes, and analyze the relationships between surrounding blocks.

Objective: The purpose of this study is to identify the factors that drive EHR acceptance among nurses aged 55 years and older.Design: A Technology Acceptance Model (TAM) based questionnaire was administered to the nursing staff of a Canadian healthcare facility that had adopted a new EHR system three years prior.Results: A total of 126 nurses completed the survey (28% response rate). Using partial least squares analysis and multi-group comparison analysis, significant differences were found in acceptance factors among various age groups. The most significant factors for nurses 55+ were the subjective norms within the organization, the image/status the system could bring to the worker, and the perceived ease of use of the system.Conclusions: To better tailor future EHR implementations to the increasing number of older nurses in the workforce, managers must: display clear support for the system themselves, as well as underscore the ease of the system and the status it can bring to the worker within the organization.

Connectivism is a learning theory that is designed for the digital age. Students in today’s classrooms are digital natives who have unique learning styles. Connectivism has been studied primarily in online college classes and has been shown to increase motivation and student achievement. However, connectivism has not been widely explored in traditional K-12 classroom settings. Implementing connectivist learning strategies in traditional K-12 classrooms can bridge the gap between the needs of digital natives and K-12 education. The purpose of this paper is to present practical strategies for implementing connectivist learning strategies in the traditional K-12 classroom. Five strategies for implementing connectivism in the traditional K-12 classrooms are (1) shifting from teacher-centered to student-centered pedagogy, (2) incorporating technology with readily-available devices, (3) never providing information that students can access themselves, (4) incorporate and practice utilizing technology networks, and (5) incorporate and practice utilizing social networks.

Local currencies are a special form of digital cash that are used for payments within a contained geographical area. They are made feasible today with the aid of blockchain technology that assures their security, solves the double-spend problem and offers incentives for consumer participation. They can be attractive for small businesses, encouraging members of the community to engage in commercial transactions within the area. Consumers are motivated to support local merchants, while enjoying a simple way to pay and the possibility of earning rewards based on cryptocurrency. In this case study, key issues are explored in the local community of Tel Aviv.

Business Intelligence & Analytics’ dashboards, data visualizations, and visual analysis systems are cognitive tools that heavily rely on our understanding of subconscious-level processing, human perception and cognitive processes overall. These cognitive tools are the focal point of data visualization subfield called Business Information Visualization (BIV). While the BIV field has seen calls for an increased need of deploying physiological sensing techniques and the use of biometric data, these calls have yet to translate to a more comprehensive evaluation of human response to visual displays and systems using business data. This research identifies and describes biometric sensors researchers should consider in BIV, provides a brief review of literature at the nexus of data visualization and biometrics, and identifies a biometric data-based research agenda and opportunities for BIV.

With intellectual assets are more important, the enterprise pay higher attention. And the patent is an important indicator to measure the R&D results of the company. As China gradually became the leading national competition, this study will be based on Chinese listed companies for the study. Using Granger causality test to identify which patents indicators has a leading enterprise performance. Based on the difficulty of data collection, the study is only with four patent indicators and three enterprise performance indicators for verification of indicators. Patent index are including patent growth rate, patent approval rate, patent number and patent activities and enterprise performance index are net profit, return on assets (ROA) and return on net worth (ROE). Results from this study show that patents leading indicator of different industries have a great deal of difference, which also similar situations and economic leading indicator, therefore, the study recommends more industrial application of different patents leading indicator.

The digital economy has advanced from the growing investments in digital technologies by organizations in their digital transformation process. Furthermore, digital technologies are reshaping traditional business strategy leading to performance gains. In this study, we examine the notion of digital capabilities through a review of the literature. In particular, we focus on two digital capabilities, visualization and responsiveness, to examine the relationships between these digital capabilities and performance. We employed a qualitative approach to examine these relationships and interviewed 31 executives across a wide range of industries. The research makes several contributions through the conceptualization of digital capabilities and provides some preliminary results supporting our proposed conceptual framework. Our preliminary findings suggest that visualization and responsiveness, two digital capabilities, are key drivers of digital business performance.

Our work focuses on user trust and the impact of UI design on business professionals’ level of engagement when using Machine Learning (ML) recommender applications to accomplish their business tasks. In order to achieve the purpose, the case study is Resume Matching; a web-based application which aims to provide recommendations of the best matched talents for recruiters. Due to the nature of the service provided by Resume Matching and its target users, we need to design its UI to engage recruiters to gain trust on the automatic recommendation. Till this end, we have created an iterative and incremental process of designing and testing the UI components of Resume Matching. This paper presents our design and testing processes, our findings and a set of guidelines on Machine Learning User Trust Improvement (ML-UTI) Research Model for increasing user trust level to end users through effective UI design.

Applying data-driven science to find out interesting patterns for enterprise has been the focus of research issues in recent years. With the advances in information technology and network infrastructure, huge amount of data can be instantly analyzed, interpreted, and visualized by scientists. Based on the existing literature in data science, the topics of most prior studies are emphasized on mathematical equations, algorithm evaluation, model development, and machine learning. Data science studies based on the application of social network are comparatively few. The purpose of this study is to elaborate the key concepts of social network analysis (SNA) and its plausible business applications for enterprise. SNA is not a novel application, in contrast, it is a mature tool and has been applied in the field of organizational behavior and management science. In general, SNA software (e.g., UCINET or Pajek) is often analyzed based on given or closed data. However, this study suggests that it can be developed as cloud service due to the progress of cloud computing. Most importantly, the notion of SNA can be applied as various applications in different industries, such as marketing, healthcare, and talent development. Its computations and applications need to be highlighted. This study aims to fill this gap and illustrate how to apply SNA to explore the intelligent patterns from data.

The objective of the work is the research and development of a web-based visualization system for the creation and testing of zoomable projection cards. The basic idea is to project a multidimensional data set onto two dimensions using projection methods to represent it on a 2D surface. Based on the Card, Mackinlay, and Shneiderman visualization pipeline, a data processing model has been developed. For data processing various distance metrics, dimension reduction methods, zooming approaches as well as presentation concepts are considered. The peculiarities and considerations of the respective technology are discussed. A zooming approach allows large amounts of data to be displayed on a limited area. In order to better visualize connections within the data, concepts of presentation are discussed. The data points are represented as glyph-based objects or using color maps, various shapes, and sizes. Best practices about colormaps are discussed. In order to display large amounts of data in real time, a separation of the generation and visualization process takes place. During generation, a tabular file and selected configuration execute computationally-intensive transformation processes to create map material. Similar to Google Maps, the generated map material is represented by a visualization. Management concepts for managing various map sets as well as their generation and presentation are presented. A user interface can be used to create and visualize map material. The user uploads a tabular file into the system and chooses between different configuration parameters. Subsequently, this information is used to generate map material. The maps and various interaction options are provided in the visualization interface. Using various application examples, the advantages of this visualization system are presented.

Research on actionable data analytics has garnered considerable attention in recent times. Organizations are emphasizing on analytics as a competitive advantage enabler. Significant business and information systems research has been done to assess the value associated with analytics and to further its scope. Outcome of such research shows a need for organizations to effectively use analytics to generate, develop and use new insights in their operations. An active research area is reducing knowledge gap and bridging the action distance to realize an immediate value. To address this research gap, my focus is on operationalizing analytics to minimize action distance and bridge knowledge gap in the context of strategy and operations. Using design science research approach, this research aims to model how analytics can be used pervasively within an organization. This model is referred to as Composite Application Model for operationalizing analytics. The Composite Application Model for analytics will be helpful in minimizing the knowledge gap and action distance between the strategy and operations in organizations. This research contributes to expand the current practice of enterprise performance management and establishes a novel and intelligence based control within organizations.

Organizations face challenges in gathering, interpreting, and acting upon real-time data. To overcome the challenge of real time data interpretation, most retailers have built business dashboards to readily display key performance indicators (KPIs). The most critical element of a dashboard is the user interface and how KPIs are displayed to end users for interpretation. Given the importance of visual analytics and business dashboard design, Williams-Sonoma, Inc (WSI) tasked the supply chain solutions (SCS) team to develop a comprehensive strategy for gathering, interpreting, and acting upon real time data within its fulfillment centers. Prior to the implementation, operational leadership had limited visibility to people, processes, and systems within the fulfillment centers. Using a bottom-up design approach, the SCS team identified and bridged the gap between two order management systems and created end to end flow visibility. With an emphasis on providing visibility to the “right information at the right time” SCS created a Flow Management platform which predicts operational risks by intercepting real time order drops and worker activity. The flow management platform is a minute-by-minute supply chain monitoring system that displays a set of related KPIs from various stages (e.g. wave, pick, pack, ship) of the order fulfillment process. After the implementation, leadership of fulfillment centers had access to a business intelligence framework that linked two key order management systems and enabled them to develop an optimal strategy for best serving our customers. It also allowed leadership to identify gaps in the order fulfillment and prioritization process.