Exploring Affective Computing for Enhancing the Museum Experience with Online Collections
Gunho Chae, KAIST, Republic of Korea / S. Joon Park, Drexel University, USA / Robert Stein, Indianapolis Museum of Art, USA / Jungwha Kim, KAIST, Republic of Korea / Susan Wiedenbeck, Drexel University, USA
Today, museums observe an increasing number of “untrained eyes” visiting their online collections to browse or search art collections for intrinsic enjoyment and leisure. They seek online experiences that can be fun and pleasurable, rather than a suite of advanced features. Our study proposes a new concept of an online art collection search system that can help users determine their interests in art. AMARA, an affective question-based agent using social tags, has been used to study how an affective system can influence users’ affective states, levels of engagement, and perceptions of meaningful online museum experience. We conducted a between-group experiment using three different experimental groups. Lastly, we present our preliminary results that confirmed the significance in levels of pleasurable interaction, engagement, and meaningful museum experience, followed by a discussion and implication of our findings.
Keywords: online collections, affective embedded agent, emotional interaction, social tagging, and collection search
In the recent past, museums have attempted to implement technology-mediated methods to increase the accessibility of online art collections and improve the online experience for visitors (Rayward and Twidale, 1999; Dyson and Moran, 2000; Trant and Wyman, 2006; Marty 2011). Despite the various attempts to improve this experience, the museums still lack an affective environment that can help “the untrained eyes” (Rayward and Twidale, 1999) to determine their interests in museum collections based on the affective states and preferences of visitors. However, museums’ online collections still remain a difficult place for the public to access, due in part to a focus on facilitating access and discovery for subject-matter experts to seek information about objects in the collections (Rayward and Twidale, 1999; Bowen and Filippini-Fantoni, 2004).
Today, museums observe an increasing number of untrained eyes visiting their online collections to browse or search art collections for intrinsic enjoyment and leisure (Skov and Ingwersen, 2008; Packer, 2010; Marty, Sayre, and Fantoni, 2011). These novice visitors are not experts and may not have deep knowledge about art, which makes it difficult to search for artworks on museum websites (Smith, 2006; Chan, 2007). They may be motivated to search online collections, but lack the formal knowledge of art history to correctly formulate effective search queries. Museums’ online collections lack the appropriate affective and hedonic values necessary to support these art enthusiasts’ needs. The emotional components and social interactions present in a physical museum are some of the goals in online museum experience. Therefore, to properly support the needs of these novice users, it is necessary to take holistic approaches for online museum experiences, which go beyond accessibility and functionality (Johnson, Gardner, and Wiles, 2004).
In this paper, we first discuss the current challenges facing online museum collections. Second, we propose holistic approaches to creating a museum experience in the context of emotional interaction. Third, we introduce a new concept for the design of an online art collection search system, AMARA (Affective Museum of Art Resource Agent). This affective question-based agent using social tags as its search mechanism was designed to help online visitors determine their known and unknown interests in art by addressing the aspect of emotional interaction and personal preference. The remainder of this paper describes the results of a between-group experiment we conducted using three different experimental groups. We present our preliminary results and implications to the museum community. Finally, we discuss our future plans and next steps in our research.
2. Challenges in online museum experience
Primary goals in museum experience include increasing the visitors’ awareness of available collections and resources, guiding them in various ways to interesting collections, and boosting their levels of experience (Falk and Dierking, 1992; Rayward and Twidale, 1999; Trant and Wyman, 2006; Marty, 2007a). Museums have been developing online environments in order to achieve these goals. For example, online museums use the cyberdocent in the virtual tour environment to provide informative commentary (Rayward and Twidale, 1999; Almeida and Yokoi, 2003). The social-tagging system has been adopted to help increase the accessibility of the online collections (Chun et al., 2006; Smith, 2006; Chan, 2007; Trant, 2009; Chae and Kim, 2011a). The personalized digital collection system is another application for the online visitors to create their own galleries (Fantoni and Bowen, 2007; Marty, 2011; Marty, Sayre, and Fantoni, 2011). However, online museums still face challenges that need to be taken into consideration.
First of all, art collections are not always accessible for the online visitors because the collection information is primarily centered on experts and artists (Hamma, 2004; Smith, 2006; Trant and Wyman, 2006; Marty, 2007a). Online museums are trying hard to find methods to successfully deliver art collections to users by developing concrete and well-defined collection development policies and metadata schemes, and incorporating technologies to enable easy access to the collections (Weng and Mi, 2006). For example, searchable descriptive metadata increases the likelihood that the digital content will be discovered by the online visitors (NISO, 2004), yet it still requires users’ knowledge, expertise, and the ability to form a successful query in order to be effective. Similarly, improving the accessibility of online collections using social tagging systems has been studied widely (Chun et al., 2006; Smith, 2006; Chan, 2007; Trant, 2009), but implementing it by itself may not be sufficient in increasing accessibility while maintaining the visitors’ levels of intrinsic motivation and enjoyment (Chae and Kim, 2011a).
Furthermore, museums’ online collection have not always been successful in providing meaningful experiences that can motivate the visitors to further explore what is available to them. Today, an increasing number of online visitors demand experience-centered interaction that can be enjoyable and easy as they tend to visit online museums for hobby and leisure purposes (Skov and Ingwersen, 2008; Packer, 2010; Marty, Sayre, and Fantoni, 2011). Unlike physical museums, however, online museum collections lack an affective environment that takes into consideration visitors’ experiences, such as feeling, mood, and desire. When interacting with the online collections, visitors experience a variety of emotional reactions or responses, including enjoyment, satisfaction, and possibly other negative emotions from the experience itself and the collections they happen to view. These different types of emotions are largely ignored in the current implementations of online collections and can be significant channels for expressing meaningful experience when designing such an online experience. Systematic efforts are required to engage the untrained online visitors to experience the artworks and help them determine their interests in artwork, which in turn can intrinsically motivate them to further explore the online and offline museum. Thus, it is necessary to emphasize holistic approaches to online museum visitors experience, which look beyond usability and functionality, to address these issues (Johnson, Gardner, and Wiles, 2004; Pallud, 2008).
3. Emotional interaction in online museum experiences
Human-computer interaction researchers argue that systems built on models of cognition must also address affect to bridge the gap between the user and system by placing the domain of human experience at the center of interaction (Boehner et al., 2005; Wright and McCarthy, 2010). Emotional interaction is concerned with how users feel and respond when interacting with systems (Preece, Rogers, and Sharp, 2011). It takes into consideration users’ experience, such as desires, values, and feelings (Tractinsky, Katz, and Ikar, 2000). Design for emotional interaction, or affective computing, has led to the development of systems that convey emotional components to improve user experience. Emotion can bridge the gap between the visitors and online museum by placing the domain of human experience at the center of interaction (Boehner et al., 2005; Wright, Wallace, and McCarthy, 2008). Consequently, designing systems to respond to users’ emotions and studying affective consequences on users are becoming valued experience-centered goals for the future of online museum experience.
Affective computing focuses on designing interactive systems that elicit positive emotions, such as fun, enjoyment, and intrinsic motivation. Embedded agents in particular have been used to study users’ emotional responses and to enhance interactions with systems (Beale and Creed, 2009). This anthropomorphic approach can potentially improve the quality of computer-mediated experiences by encouraging positive social interaction and reducing negative emotions for the users. Researchers found that an embedded agent, which is empathic or sympathetic toward the users, whether it is the use of facial expressions or textual content, was generally rated more positively by participants when compared with an agent that was not emotional toward them (Picard, 2000; Beale and Creed, 2009). The findings about affective agents imply that a system that has the ability to inquire about and respond to the user’s emotions can be perceived as more likeable, trustworthy, respectful, caring, fun, and friendly enough to continue interacting with it (Picard, 2003).
Pleasurable interaction is closely related to the hedonic dimension of usability. It is a positive emotional state experienced when needs or desires are fulfilled, either from external or internal stimuli (Holt and Lock, 2008). When a system is designed to take joy-of-use factors into account, users’ levels of enjoyment can increase, along with the overall rating of the system. The factors that make such interaction enjoyable could be closely related to the interaction design attributes, such as controllability, simplicity, ease of use, and usefulness of the system in relation to the aesthetic qualities of its design.
Furthermore, pleasurable interaction may help establish users’ attitudes towards the system by improving their performance, affecting their satisfaction, and influencing their willingness to adopt the system (Tractinsky, 2005). Pleasurable interaction focuses on increasing the user arousal and sustaining the user’s interest and effectiveness through its user interface with emotional interaction considerations. Consequently, the concepts of pleasurable interaction can be applied to enhance museum experience through gratifying visitors’ curiosity through enjoyable interaction design that contains hedonic values.
Engagement as a museum experience
The sense of engagement when interacting with art collections can be influenced by various factors, including prior knowledge, positive emotions, intrinsic motivation, time spent, and technology (Othman, Petrie, and Power, 2011). In fact, previous research suggests that engagement with art collections is significantly higher when coupled with the use of technology, such as multimedia guides and other applications for handheld devices in an in-gallery museum setting (Othman, Petrie, and Power, 2011). In an online setting, it is also plausible to theorize that engagement can be encouraged by eliciting visitors’ positive emotions through software-based user-interaction techniques, such as embedded agents. However, little study has been conducted to discover how an online museum environment can positively influence the sense and level of engagement for the visitors.
Meaningful museum experience
It is important for a museum to take into consideration the visitors’ experience as a whole. Meaningful experience can be established among people, people and art collections, and people and the museum as a whole. In an in-gallery museum setting, meaningful experiences can be enhanced by exhibitions, art collections, or other visitors (Othman, Petrie, and Power, 2011). On the other hand, in an online museum setting, a meaningful experience can be achieved by emotional interaction that can bridge the gap between the visitors and the art collections by creating an environment that can increase intrinsic motivation and satisfying aspect of their visits. Furthermore, their positive and meaningful online experience can lead to a willingness to visit offline museums to have another level of connections with the exhibits. Emotional interaction can support the online visitors to experience meaningful connections with the artworks.
4. Research design
In our study, we designed and developed AMARA (Affective Museum of Art Resource Agent) as an affective question-based search agent to help the online visitors determine their interests and preferences in art. AMARA interacts with untrained individuals who may have general interests in the art museum but may not know where to start, as it does not require the users to have specific goals or purposes in finding artworks. AMARA uses a social-tagging system as the image-retrieval technique, which is described in detail in the following section. The social-tagging systems can enhance the search results for the novice visitors by retrieving artworks that may be relevant to them based on similar tags submitted by other users of the system.
AMARA is an academic-industrial research collaboration initiated by KAIST and Drexel University. Drexel University and KAIST took parts in system design conceptualization, AMARA frameworks, AMARA search architecture, user-experience design, and development. The Indianapolis Museum of Art (IMA) website was used as the platform for this study to design and implement the affective embedded agent using its collection database, social-tagging data, and other coding sources.
User experience architecture
Unlike most search engines or image retrieval systems using the classic keyword search box, our system, AMARA, uses interactive techniques that are easy to use and sensitive to the emotional components of the artwork retrieval process. AMARA is especially designed to aid untrained online visitors to determine their interests in art collections. The online visitor does not need to be an expert in locating their preferred artworks or have specific purposes or goals in searching for interesting artworks, because AMARA does not require specific information, such as the name of the artist or the title of the artwork. The animated agent asks a few questions regarding users’ mood, interests, and preferences in arts. Based on the answers provided by the users, which are mapped with pre-assigned social tags, the system displays the results that can be particularly interesting to the user.
Social tags as an artwork search technique
AMARA uses the social-tagging system as a core technology of collection search technique. Social tags, also referred to as user-applied keyword metadata, have been used in practice to encourage user participation and to improve accessibility. Research by the Steve.museum social tagging project has demonstrated that the language used by novice visitors to describe works of art differs significantly from that descriptive metadata used by museums to describe the same collections (Trant, 2009). In other words, social tags can be used as a method that can provide a consensus around the contents, which can be especially effective for the untrained eyes. Using social tags, a user can directly participate in the interpretation of collections by tagging a word and understanding collections through sharing such interpretations with other users. The social-tagging systems have paved the way for museum novices to access the once-difficult online collection more easily (Chun et al., 2006; Smith, 2006; Chan, 2007; Trant, 2009).
AMARA interacts with the users by asking simple questions in texts (figure 1). Predetermined social tags are assigned to each one of the multiple choice answers. When the user selects an answer, AMARA will remember the artworks with corresponding tags and then run the search at the end of the session.
Figure 1: A screen shot of AMARA
For our preliminary study, we developed a total of 190 questions and corresponding multiple-choice answers that AMARA interacts with using the social tags from the IMA. We used a constructive question–answer developing technique by adopting the six dimensions of classifications of tags: background, identification, theme, association, emotion, and figure (Chae and Kim, 2011b). According to the dimensions, we carefully developed the questions and answers that can interest online visitors. This effort was done manually rather than with automated methods in order to create a set of various questions and answers for each topic of interest. The questions developed are from general preferences in everyday life to specific interests in types of art and era. We then identified recurring social tags to ensure the significance of the consensus that have been made by the end-users. In order to do so, we analyzed 318,810 social tags and determined the 5,160 recurring social tags in the IMA social tags database. After careful sorting and analysis, we mapped appropriate social tags to each answer to show relevant artworks as results.
Designing the affective question-based search agent
The affective agent is designed to be a female docent with animation effects. Previous studies suggest that female agents (Beale and Creed, 2009) and use of animation (Dehn and Mulken, 2000) can help systems be perceived as more empathic, interesting, and enjoyable (figure 1). AMARA will be present at all times on a fixed area so that it will not interfere with the main page (figure 2).
Figure 2: AMARA in the context of the IMA website
When the user starts the system, AMARA introduces herself and proceeds to ask five simple multiple choice questions, two of which contains questions about users’ current feelings, and one open-ended question at the end. While answering the questions, users can hit the SEARCH button whenever they want to see the results. When the results are displayed, the agent asks the user if he or she wants to repeat the search again. At any point during the process, users may skip the question, or go back to previous questions to change their answers.
5. Empirical study
We conducted a controlled group study to evaluate whether AMARA can enhance the affective online museum experience. The study was designed to investigate the impact of the affective search agent without requiring a keyword search on users’ affective states, level of engagement, and meaningful museum experience. A between-groups experiment was developed with one main factor (system) including three different experimental groups: experimental group 1 (control group: search box only), experimental group 2 (text-only agent and search box), and experimental group 3 (AMARA and search box) (figure 3). The control group was a replication of the IMA website’s search page. The design and aesthetic components for all three groups were kept identical to avoid bias in measuring pleasurable interaction and museum experience. There are three dependent variables, which include pleasurable interaction, engagement, and museum experience.
Figure 3: Experimental group 1 (search box only), experimental group 2 (text-only agent and search box), and experimental group 3 (AMARA and search box), respectively
The experiment consisted of three stages. First, participants were asked to answer the pre-session survey regarding demographic and museum experience–related questions as well as computer confidence using the online survey tool.
Next, participants were asked to use one of the three systems that we developed to work on tasks based on a scenario. Two different scenarios are prepared, and a scenario was given to the participants randomly. The scenarios focus on the complementary relationship between the physical museum and online museum (Chadwick and Boverie, 1999; Marty, 2007b). One scenario focused on visiting the online museum in order to preview some art collections and provide recommendations to a friend before visiting the physical museum. The other scenario focused on visiting the online museum after briefly experiencing the physical museum. Their tasks were to select artworks that they preferred to see before or after a visit to the IMA in Indianapolis and provide certain information, such as the image of art, URL of the page, title, artist, and creation date.
After completing the tasks, participants were asked to answer the post-session survey using the online survey tool. The questionnaires on pleasurable interaction, engagement, and meaningful museum experience used in the experiment were validated multi-item rating scales. Lastly, we asked participants a few open-ended questions to record their experience in using the system.
A total of twenty-one participants were recruited for this study. The mean age was 28.8 with a range of 23 to 57; eleven of them were male, and ten of them were female. Of the participants, 23.8 percent answered that they had never visited an online museum collection, and 40.1 percent that they use similar online collections one to three times a year. One participant had never been to a physical museum, and 49.6 percent of the participants answered that they go to a museum one to three times a year. None of the participants has been to IMA in Indianapolis, Indiana. Of the participants, 83.3% answered that they have a little or some knowledge about art. Two participants have been to the IMA website prior to this study. All participants were undergraduate and graduate students currently enrolled at Drexel University.
A multivariate analysis of variance confirmed that the different experimental groups (search box only, text-only agent and search box, and AMARA and search box) had a significant effect across all three variables, which include pleasurable interaction, engagement, and meaningful experience (Wilks' Lambda =.093, F[6,32] = 12.195, p < 0.001). In addition, a one-way, between-subjects ANOVA was conducted to determine the individual effect of types of experimental group on the three variables. The results confirmed that there was a significant between-groups effect on all variables (p < .05). However, there were no significant effects of the participants’ backgrounds (knowledge in art and whether they have been to online and offline museums) or computer confidence on any of variables across the three experimental groups.
Figure 4: Result of levels of pleasurable interaction
The result indicates that a significant main effect was found for pleasurable interaction (F[2,18] = 35.091, p < .001). In other words, the presence of AMARA and text-only agent has an effect on user perceptions of pleasurable interaction. Post-hoc comparisons with Tukey’s HSD indicated that the mean rating of pleasurable interaction for experimental group 3 (AMARA and search box) (M = 6.15, SD = 0.60) was significantly higher than experimental group 2 (text-only agent and search box) (M = 4.95, SD = 0.49; p = .009). Levels of pleasurable interaction under experimental group 1 (search box only) (M = 3.03, SD = 0.89) were significantly lower than under experimental group 2 (p = 0.047) and under experimental group 3 (p < 0.001) (figure 4). The result revealed that participants perceived the online museum with AMARA to be more pleasurable to interact with than the online museum with the text-only agent. It also indicates that the online museum with keyword search boxes to be less pleasurable to interact with than the online museum with the text-only agent.
Figure 5: Result of levels of engagement
There was a significant difference on users’ levels of engagement (F[2, 18] = 26.665, p < .001). Post-hoc comparisons with Tukey’s HSD indicated that ratings of engagement under experimental group 3 (AMARA and search box) (M = 5.867, SD = 0.89) were significantly higher than under experimental group 1 (search box only) (M = 3.167, SD 1.32; p < .001). Similarly, levels of engagement under experimental group 2 (text-only agent and search box) (M = 5.100, SD = 1.27) were significantly higher than under experimental group 1 (M = 3.167, SD 1.32; p < .001). However, ratings of engagement under experimental group 2 (M = 5.10, SD = 1.27) did not significantly differ from experimental group 3 (M = 5.867, SD = 0.89; p = 0.309) (figure 5). The result indicated that the participants using AMARA did not necessarily feel more engaged with the museum website than participants using the text-only agent.
Figure 6: Result of levels of meaningful experiment
The difference was significant for ratings of museum experience (F[2, 18] = 12.685, p < .001). Post-hoc comparisons of the three experimental groups with Tukey’s HSD indicated that levels of museum experience for experimental group 3 (AMARA and search box) (M = 5.76, SD = 1.04) were significantly higher than experimental group 1 (M = 3.83, SD =1.21; p = .004). However, ratings of museum experience under experimental group 2 (text-only agent and search box) (M = 5.23, SD = 0.56) did not significantly differ from experimental group 3 (M = 5.76, SD = 1.04; p = 0.583) nor from experimental group 1 (M = 3.83, SD =1.21; p = 0.058) (figure 6). The result indicated that the participants using the IMA website with the text-only agent did not necessarily perceive their interaction with art collections less meaningful than the ones who used AMARA. Also, participants who used the IMA website with the text-only agent did not perceive their interactions with art collections more meaningful than the ones who used the websites with the keyword search boxes.
Significant Differences Between:
Search Box and Text-only agent
Search Box and AMARA
Text-only agent and AMARA
Table 1: Summary of the results of hypothesized differences between the three groups for all three variables
One of the most encouraging findings of our study is the high correlation between pleasurable interaction and the use of AMARA, a question-based affective agent for artwork search. Our goal was to introduce a new concept in art collection search for the untrained visitors who may have interests in art but do not have sufficient knowledge to initiate specific search queries. Our results confirmed that AMARA induced positive emotions for the visitors when interacting with the IMA website. Participants rated the IMA website with AMARA to be creative, innovative, exciting, and easy, compared to the website with text-only agent or the website with the keyword search boxes only. They perceived the IMA website to be more pleasurable to look at and thought the website evoked positive feelings. Three participants with enough or a lot of knowledge in art rated the IMA website with AMARA to be fun, enjoyable, and cool to use. It is interesting to have this result because these participants did not use AMARA for the entire session, as they utilized the keyword search box more than other participants with less knowledge in art. This implies that AMARA can elicit positive emotion for not only untrained visitors, but also art experts.
The result shows that AMARA and the text-only agent can help online visitors get engaged with the art collections and exhibits. The participants who used the IMA website with AMARA and the text-only agent felt that they were experiencing the museum, rather than just looking at the collections. They also answered that their visit to the IMA website was inspiring. We determined that the level of engagement is correlated with pleasurable interaction, as participants who experienced pleasurable interactions using AMARA and text-only agent also showed high levels of engagement. However, no significant difference has been found between the use of AMARA and the text-only agent in terms of levels of engagement as shown in Table 1. This implies that even a simple text-only, question-based search agent that interacts with the online visitors by providing emotional natural language can create an online museum environment that enables a certain engagement with art collections.
Another finding reveals that the participants using the IMA website with AMARA perceived their experience to be more meaningful than those using the website with text-only agent or classic keyword search boxes. These participants felt a strong sense of wonder about artworks and put a lot of effort into thinking about the artworks. They also answered that they were still interested to know more about the topic of the artworks after completing their tasks. In fact, two participants from experimental group 3 made a personal note about particular artworks and/or artists to further explore them later. We can insinuate that AMARA, an animated affective search agent, created a material and immaterial museum experience that is intrinsically motivating for the participants and allowed them to explore further artworks they would not have otherwise seen. For example, a participant mentioned that he felt an interesting level of serendipity using AMARA, which motivated him to reflect on the significance of the artworks and their meanings.
Interestingly, all nine participants who used the IMA website with AMARA said they would visit the website again. By contrast, only four (out of six) participants using the website with the traditional keyword search box and five (out of six) using the website with the text-only agent said they would visit it again. This is an encouraging result and suggests that online museums can increase potential visitors by including affective interactive technologies in their sites, as well making more apparent the connections between the online and offline museums.
7. Conclusion and future work
Given the rising population of novice visitors to online museum collections, the focus of those experiences on providing information-retrieval tools for experts is no longer sufficient. The addition of affective design principles to online collection experiences suggests a paradigm shift in online art collection search and meaningful museum experience. The use of AMARA confirmed that participants enjoy an experience-centered art collection search, as compared with the familiar suite of advanced search features.
Our current research focuses on the interactive aspects of museum experiences by investigating the emotional context implicit in online museum collections. In future studies, we plan to explore how pleasurable interactions, level of engagement, and the perception of meaningful experiences affect online visitors’ decision-making results (e.g., how they perceive the search results to be relevant and whether their online experience motivates them to visit the offline museum).
It is surprising to find that there were no significant effects of the participants’ backgrounds, especially the knowledge about art. We suspect that it is due to relatively small sample size and the high level of education of the participants. More data are required to clarify the connection between the affective online museum experience and depth of knowledge in art.
In addition, a few participants mentioned that they would like to see more diverse questions that cover various areas of our lives and art in general. We plan to continuously develop meaningful questions for the online visitors to answer.
We hope that our research serves to broaden our understanding of the role that emotions play in use of collection search and retrieval systems and that it will provide useful guidelines for the online museum community in studying interactions with systems that are intelligent and sensitive to online visitors’ emotions and experience in general.
We thank the members of the Indianapolis Museum of Art lab for assisting our technical needs. We also thank Sangsoo Nam at KAIST for his technical expertise.
Almeida, P., and S. Yokoi. (2003). “Interactive Character as a Virtual Tour Guide to an Online Museum Exhibition.” In D. Bearman and J. Trant (eds.). Museums and the Web 03: Proceedings. CD ROM. Archives & Museum Informatics. Available at: http://www.museumsandtheweb.com/mw2003/papers/almeida/almeida.html
Beale, R., and C. Creed. (2009). “Affective interaction: How emotional agents affect users.” International Journal of Human-Computer Studies, 67, 755–776.
Boehner, K., R. DePaula, P. Dourish, and P. Sengers. (2005). “Affect: From Information to Interaction.” Proceedings of the 4th decennial conference on critical computing: between sense and sensibility. Aarhus, Denmark, August 21–25, 59–68.
Bowen, J., and S. Filippini-Fantoni. (2004). “Personalization and the Web from a museum perspective.” In D. Bearman and J. Trant (eds.). Museums and the Web 2004: Proceedings. Toronto: Archives & Museum Informatics. Available at: http://www.museumsandtheweb.com/mw2004/papers/bowen/bowen.html
Chadwick, J., and P. Boverie. (1999). “A survey of characteristics and patterns of behavior in visitors to a museum web site.” In D. Bearman and J. Trant (eds.). Museums and the Web 1999. Pittsburgh, PA: Archives & Museum Informatics. Available at http://www.archimuse.com/mw99/papers/chadwick/ chadwick.html
Chae, G., and J. Kim. (2011a). “Rethinking Museum Management by Exploring the Potential of Social Tagging Systems in Online Art Museums.” The International Journal of the Inclusive Museum, 3(3), 131–140.
Chae, G., and J. Kim. (2011b). “Can Social Tagging Be a Tool to Reduce the Semantic Gap between Curators and Audiences? Making a Semantic Structure of Tags byIMplementing the Facetted Tagging System for Online Art Museums.” In J. Trant and D. Bearman (eds). Museums and the Web 2011: Proceedings. Toronto: Archives & Museum Informatics. Available at: http://www.museumsandtheweb.com/mw2011/papers/can_social_tagging_be_a_tool_to_reduce_the_sem
Chan, S. (2007). “Tagging and Searching-Serendipity and museum collection databases.” In D. Bearman and J. Trant (eds.). Museums and the Web 2007: Proceedings. Toronto: Archives & Museum Informatics. Available at: http://www.archimuse.com/mw2007/papers/chan/chan.html
Chun, S., R. Cherry, D. Hiwiller, J. Trant, and B. Wyman. (2006). “Steve. museum: an ongoing experiment in social tagging, folksonomy, and museums.” In D. Bearman and J. Trant (eds.). Museums and the Web 2006: Proceedings. Toronto: Archives & Museum Informatics. Available at: http://www.museumsandtheweb.com/mw2006/papers/wyman/wyman.html
Dehn, D.M., and S.V. Mulken. (2000). “The impact of animated interface agents: a review of empirical research.” International Journal of Human-Computer Studies, 52, 1–22.
Dyson, M., and K. Moran. (2000). “Informing the design of Web interfaces to museum collections.” Museum Management and Curatorship, 18, 391–406.
Falk, J.H., and L.D. Dierking. (1992). The Museum Experience. Washington DC: Howells House.
Fantoni, S., and J.P. Bowen. (2007). “Bookmarking in museums: Extending the museum experience beyond the visit?” In D. Bearman and J. Trant (eds.). Museums and the Web 2007. Toronto: Archives & Museum Informatics. Available at: http://www.archimuse.com/mw2007/papers/filippini-fantoni/filippini-fanto...
Hamma, K. (2004). “The role of museums in online teaching, learning, and research.” First Monday, 9(5). Available at: http://firstmonday.org/issues/issue9_5/hamma
Holt, J., and S. Lock. (2008). "Understanding and Deconstructing Pleasure: A Hierarchical Approach." CHI 2008, April 5–10.
Johnson, D., J. Gardner, and J. Wiles. (2004). “Experience as a moderator of the media equation: the impact of flattery and praise.” International Journal of Human-Computer Studies, 61, 237–258.
Marty, P.F. (2007a). "The changing nature of information work in museums." Journal of the American Society for Information Science and Technology, 58(1), 97–107.
Marty, P.F. (2007b). "Museum Websites and Museum Visitors: Before and After the Museum Visit." Museum Management and Curatorship, 22(4), 337–360.
Marty, P.F. (2011). “My lost museum: User expectations and motivations for creating personal digital collections on museum websites.” Library & Information Science Research, 33(3), 211–219.
Marty, P.F., S. Sayre, and S. Fantoni. (2011). “Personal digital collections: Involving users in the co-creation of digital cultural heritage.” In G. Styliaras, D. Koukopoulos, amd F. Lazarinis (eds.). Handbook of research on technologies and cultural heritage: Applications and environments. Hershey, PA: IGI Global. 285–304.
NISO (NISO Framework Advisory Group). (2004). A Framework of Guidance for Building Good Digital Collections, 2nd ed. Bethesda, MD: NISO Press. Available at: http://diamond.temple.edu:81/search/
Othman, M.K., H. Petrie, H., and C. Power. (2011). “Engaging Visitors in Museums with Technology: Scales for the Measurement of Visitor and Multimedia Guide Experience.” HUMAN-COMPUTER INTERACTION – INTERACT 2011, Lecture Notes in Computer Science, 2011, Volume 6949/2011, 92–99.
Packer, J. (2010). “Beyond Learning: Exploring Visitors’ Perceptions of the Value and Benefits of Museum Experiences.” Curator: The Museum Journal, 51(1), 33–54.
Pallud, J. (2008). "The Role of Authenticity in the Experience of Visitors Interacting with Museum Technologies." SIGHCI 2008 Proceedings. Paper 12. Available at: http://aisel.aisnet.org/sighci2008/12
Preece, J., Y. Rogers, and H. Sharp. (2011). Interaction Design Beyond Human-Computer Interaction, 3rd Edition. New York, NY: John Wiley & Sons, Inc.
Picard, R.W. (2000). Affective Computing, Paperback edition. Cambridge, Massachusetts: MIT Press.
Picard, R.W. (2003). “Affective computing: challenges.” International Journal of Human-Computer Studies, 59, 55–64.
Rayward, W.B., and M.B. Twidale. (1999). “From Docent to Cyberdocent: Education and Guidancein the Virtual Museum.” Archives and Museum Informatics, 13(1), 23–53.
Skov, M., and P. Ingwersen. (2008). “Exploring information seeking behaviour in a digital museum context.” Proceedings of the second international symposium on Information interaction in context. October 14–17, 2008. London, United Kingdom.
Smith, M. (2006). “Viewer tagging in art museums: Comparisons to concepts and vocabularies of art museum visitors.” In Advances in classification research, 17: Proceedings of the 17th ASIS&T SIG/CR Classification Research Workshop.
Tractinsky, N. (2005). “Aesthetics in Information Technology: Motivation and Future Research Directions.” In P. Zhang, and D. Galletta (eds.). Human-Computer Interaction and Management Information Systems: Foundations. Armonk, NY: M.E. Sharpe, Inc., 330–347.
Tractinsky, N., A.S. Katz, and D. Ikar. (2000). “What is beautiful is usable.” Interacting with Computers, 13(2), 127–145.
Trant, J. (2009). “Tagging, Folksonomy and Art Museums: Results of steve.museum’s research.” Archives & Museum Informatics. Available at: http://verne.steve.museum/SteveResearchReport2008.pdf
Trant, J., and B. Wyman. (2006). “Investigating social tagging and folksonomy in art museums with steve. Museum.” The Collaborative Web Tagging Workshop (WWW'06).
Weng, C., and J. Mi. (2006). “Towards accessibility to digital cultural materials: a FRBRized approach.” International digital library, 22(3), 217–232.
Wright, P., and B. McCarthy. (2010). "Experience-Centered Design: Designers, Users, and Communities in Dialogue." Synthesis Lecture on Human-Centered Informatics. San Francisco, CA: Morgan and Claypool Publishers.
Wright, P., J. Wallace, and J. McCarthy. (2008). “Aesthetics and Experience-Centered Design.” ACM Transaction on Computer-Human Interaction, 15(4), article 18.