Social Responses to Computers in Cloud Computing Environment: The Importance of Source Orientation

Social Responses to Computers in Cloud Computing Environment: The Importance of Source Orientation Sungmoo Hong Department of Interaction Science Sungkyunkwan University Seoul, South Korea gloidar@gmail.com S. Shyam Sundar Media Effects Research Laboratory Penn State University University Park, PA 16802 USA sss12@psu.edu Copyright is held by the author/owner(s). CHI 2011, May 7 12, 2011, Vancouver, BC, Canada. ACM 978-1-4503-0268-5/11/05. Abstract It is now well established that users exhibit social responses to computers. This has motivated the development of numerous social affordances for computer interfaces. Designers have exploited users tendency to treat computers as autonomous sources by building more interactivity, natural language and anthropomorphic features and functions into computer interfaces. The arrival of cloud computing complicates the design of computer interfaces, however, because individual computers may no longer be seen as autonomous sources, but rather as dumb terminals. We may be moving from the CAS (Computers as Source) model to CAM (Computer as Medium) model in humancomputer interaction [9]. Does the perceived lack of autonomy on the part of the computer undermine the socialness of human-computer interaction? If so, how does this inform interaction design of cloud-based devices? A pilot experiment is reported, along with discussion of theoretical and practical implications. Keywords Media equation, social responses, cloud computing

2 ACM Classification Keywords H.1.2. User/machine systems: Human factors, H.5.2. Information interfaces and presentation: User Interfaces, J.4. Social and behavioral Sciences: Psychology Introduction Since the birth of the Internet, people have been able to readily access collective human intelligence at any given moment through computers. The concept of cloud computing is a recent reflection of this phenomenon. It started from the idea that computers are going to be used as a public utility such as electricity and water. Cloud computing is defined by NIST as a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources [5]. The resources, e.g., servers, applications, and services, are rapidly provisioned and released with minimal management effort or service provider interaction. Historically, human computer interaction has been researched and designed by regarding the computer as an object with which people should interact by inputting commands and getting output as a result. This interactivity has made users treat computers as if they are social actors [6]. However, in a cloud computing environment, where people deal with resources in distant data centers rather than in local computers, will they still interact with computers in a social manner? Literature Review Cloud or cloud computing is a metaphor for software and desktop virtualization [8]. We can call the combination of data center hardware and software a Cloud [2]. In a cloud computing environment, computer users no longer need to own their own software applications with all of its cost [3]. The idea was borne out of economic concerns. People recognized that significant hardware resources had been left unused in worldwide data centers. Delivering computing resources from a centralized shared infrastructure has set the expectation among people that computing costs will be significantly lower than those incurred from providing their own computing [2]. Two types of cloud services, a public and private cloud service, have emerged in this industry. Public cloud services are available for business firms in a pay-asyou-go manner. They can use hardware (servers, network), which is called IaaS (Infrastructure as a Service) and/or software (publishing web sites, databases, operating systems, etc.), called SaaS (Software as a Service) in an on-demand manner. Amazon EC2 (Amazon Elastic Compute Cloud) is an example of a public cloud. Private cloud services cater to private consumers, who can use hardware (storage) and software (web applications) on demand. Microsoft Skydrive and Gmail are examples of private clouds. In sum, cloud computing refers to both the applications delivered as services over the internet and the hardware and systems software in the data centers that provide those services. That is, in a cloud computing environment, in order to perform tasks, users do not need computing resources (hardware and software) in front of them but need them in a virtualized environment, which enable users to access via thin client to on-demand-resources which are on clouds or virtual data centers. When they interact with computers in a cloud computing environment, do they interact with computers or with just monitors, mouse, keyboard and other interface tools?

3 Computer-As-Source Nass et al. [7] argued that an individual s interactions with computers are fundamentally social. In one of their initial studies, they discovered that users apply politeness norms to computers. Study participants used a single computer for the tutoring, testing, and evaluation sessions. After that, they were asked to evaluate the computer about its own performance. The questionnaire was administered in one of three ways: on the same computer that tutored, tested and evaluated them; on a paper & pencil questionnaire; or on a second, different computer. They found that participants in the same-computer condition were more positive in their evaluations. Sundar and Nass [9] suggested that humans respond to the computer as an independent source of information. They conducted two studies in order to investigate which model would be more applicable to human-computer interaction the CAS (Computer-as- Source) model or the CAM (Computer-as-Medium) model. According to the CAS model, individuals respond to computers as a source in much the same way that individuals respond to other human beings as a source. On the contrary, in the CAM model, the computer is conceptualized as a medium connecting individual with a source. Based on two experiments, the authors concluded that users do respond directly to computers in a social manner. Could it be that users treat computers socially because they perceive them as sources of information? If so, what implications does this have for computers that are not repositories of information? If computers no longer have resources housed within them, and if users are really interacting with distant data centers or other peers, they will still follow social rules when they interact with computers? In a way, the cloud environment constitutes a pure operationalization of the CAM model. However, the cloud is not simply a medium, but a combination of resources, quite akin to a group context in human communications. Computer-supported cooperative work (CSCW) presents a similar sourcing scenario and is recognized as a classic example of the CAM model [9]. Individuals using a CSCW application in a computer may focus more on others with whom they are collaborating (sources) rather than on the computer itself (medium). In fact, various CSCW studies have found that "people prefer to know who else is in a shared space" [1]. Therefore, given that in the cloud computing environment, resources of computers are no longer in local personal computers but on clouds or data centers, users may ignore features that belong to computers right in front of them and focus instead on resources in the distance, i.e., storage, application, and network infrastructure. Users may interact with computers as a dumb terminal, a mere monitor, or a medium which only displays visual information on a screen transferred from distant clouds. This would argue for lesser likelihood of applying social rules to computers connected to clouds. Therefore, H1: Individuals who use computers in cloud computing environment will be less likely to apply politeness norms to computers than those who use computers in a local computing environment.

4 Methods We conducted a pilot experiment to test this hypothesis. Specifically, in order to explore their politeness toward computers, we manipulated the nature of the computers they used (cloud vs. noncloud) and the location of their performance evaluation (on the same computer vs. different computer). Participants Thirty undergraduate and graduate students (16 males) participated were randomly assigned to each of the four groups. Their average age was 26.8 years (SD=5.23). Design Overview Given the between-subjects design, one-fourth of the participants used computers in a cloud computing environment and evaluated its performance on the same computer. Another fourth also used computers in a cloud environment but performed their interaction and evaluation on different terminals. Yet another fourth used a computer in a non-cloud environment and evaluated on the same computer. The final fourth also used a computer in a non-cloud environment, but performed their interactions and evaluations on different computers. All subjects filled out a computerbased post-questionnaire, which consisted of evaluation of the performance of computers and previous experience with computers, internet, and software or service that they had used during the experiment. Cloud vs. Non-cloud Computing Environment Web applications (e.g., web mail, online office service, music streaming service, etc.) are most popular examples of SaaS services. Personal storage services exemplify IaaS. For this study, applications for office program and personal storage service were chosen. For the non-cloud computing condition, participants used two different Office programs, Microsoft Office and Open Office. They were asked to launch the programs and create one document file, one presentation file, and one spreadsheet using each of two programs and save them on the hard disk. They did not access the internet or network infrastructure. In the cloud computing condition, participants used two different Web applications, Microsoft Office Live and Google Docs. They were asked to create one document file, one presentation file, and one spreadsheet using each of the two applications, and upload (not save on hard disks) them to online servers, i.e., clouds. Politeness If CAS (Computer-As-Source) model is applicable to human computer interaction with cloud computers, participants will rate the computer s performance more positively when they fill out the evaluations on the same computers than on different computers. The questionnaire asked participants, For each word below, please indicate how well it describes the system you have worked with. There was a list of 20 adjectives on 10-point scales that ranged from not at all to a lot. They asked about the computer s friendliness (Cronbach s α =.93, cheerful, gentle, likeable, warm, friendly, sympathetic, and affectionate), playfulness (α =.75, childlike, entertaining, enthusiastic, and playful), and effectiveness (α =.83, articulate, creative, clever, insightful, intelligent, helpful, responsive, competent, analytical). The specific items were obtained from [9].

5 Environment Non-cloud Condition SC DC Friendliness 5.38 (.74) Playfulness 3.25 (1.28) Effectiveness 7.25 (1.83) Environment Cloud 5.50 (2.73) 5.00 (2.14) 6.50 (1.77) Condition SC DC Friendliness 5.00 (1.73) Playfulness 4.00 (2.31) Effectiveness 5.86 (1.07) 5.00 (1.63) 4.29 (1.70) 5.86 (1.68) Table 1 Mean and Standard Deviation of Performance Ratings Procedures Upon entering the laboratory, participants sat in front of computers. They were given introduction sheets varied based on the condition to which they had been randomly assigned (C-SC: cloud on same computer, C- DC: cloud on different computer, NC-SC: non-cloud on same computer, and NC-DC: non-cloud on different computer). They were briefed about the procedure (to use office programs in NC-SC and NC-DC condition and office web applications in C-SC and C-DC condition) to perform a list of tasks (create and save the files on hard disk in NC-SC and NC-DC conditions and create and upload in the C-SC and C-DC conditions). After they finished their task, those who were in the C- SC and N-SC condition were asked to fill in a computerbased questionnaire by opening the Microsoft excel file on each desktop screen. Those who were in C-DC and N-DC condition were asked to move to another computer and fill in the same questionnaire. After they had finished answering the questionnaire, they were given the compensation, thanked, and dismissed. Results A series of 2 x 2 full factorial ANOVA failed to show any significant main effects or interactions, owing perhaps to the small size of the sample. However, an interesting trend was observed with the Effectiveness index. As can be seen in Table 1, subjects in the Non-Cloud condition rated the computer system as more effective when evaluating it on the same interface compared to a different terminal consistent with the demonstrated tendency to show politeness responses to computers. However, in the Cloud scenario, they did not make such a differentiation. The evaluation was less positive (compared to non-cloud) and identical across the same vs. different terminal conditions. This suggests that the Cloud scenario serves to distance the source of one s interaction. The playfulness index, which was negatively correlated with the Effectiveness index (r = -.29), behaved in a similar fashion. Assuming that playfulness is a negative attribute for a system that is meant for utilitarian tasks such as word-processing, it is not surprising that participants tended to provide lower ratings of playfulness when evaluating on the same computer rather than on a different computer. Furthermore, this difference (5.00 3.25 = 1.75) was more pronounced in the non-cloud condition compared to the cloud computing condition (4.29 4.00 = 0.29), thereby suggesting that the politeness effect is stronger in the non-cloud context. In general, there appears to be little or no difference between the same-computer and different-computer conditions in the Cloud context, across the measures. Specifically, as can be seen in Table 1, evaluation of friendliness (M=5.00) and effectiveness (M=5.86) are exactly same for SC and DC. Although we are not yet able to demonstrate statistically significant patterns, emerging data do suggest that, in cloud computing environments, users do not appear to be showing a politeness response. Discussion & Implications Preliminary findings suggest a shift in users source orientation when dealing with computers in a cloudcomputing environment. In a cloud context, users might be evaluating the system by focusing on service providers over the internet, not directly on the

6 machines in front of them. If individuals no longer respond socially to computers in clouds, principles of interaction design would undergo a fundamental change. in other words, the CAS model would be less applicable in a cloud computing environment, thus challenging the notion of a fundamentally social relationship between humans and computers [7], based on mindlessness [6]. As computing shifts to the clouds, the computers in front of us are just like monitor screens, thus suggesting that the CAM model may be more appropriate for conceptualizing and designing HCI in a cloud context. However, more experimentation is needed to discern the difference in source orientation brought about by cloud computing. We could test whether users show other social responses (other than politeness) to computers tied to the cloud. Nass and colleagues have found that users tend to view each computer terminal as a distinct self and apply personality attributes and gender stereotypes [6, 7]. An open empirical question is whether they treat cloudbased computers in a similarly human manner. Another line of inquiry could pertain to the specific devices used for accessing cloud-based services. As computing gets increasingly mobile, cloud services are likely to be accessed through a variety of hand-held devices that are smaller than a computer, e.g., tablets and smart phones. If users show differential source orientation to identical cloud services delivered through different devices, then we can conclude in favor of CAS. But, if their social responses are invariant across devices, then we would conclude support for CAM. Another possibility is that users continue to show social responses to cloud-based computers, but the magnitude is lesser than that shown toward standalone computers. This would support both CAS and CAM, but raise the possibility of multiple sources, or source layering. A portion of users social responses could be attributed to the machine in front of them while another to the distant sources identified in the cloud environment channeled through the machine. More generally, an important conceptual issue raised by the cloud computing environment is the identity of the source to which users are orienting. If people are responding to sources in the distance, instead of the machine right in front of them, who or what is the source of information that they receive and interact with? Therefore, a fundamental challenge for interaction designers is to conceptualize and operationalize these distant sources in the clouds. A whole new wave of design may center around providing source identity to cloud-based sources, in the form of creating, identifying and attributing interactions to specific sources with specific characteristics, leading to a new set of loci for human-computer interaction. Cloud-sourcing has legal implications as well, with users likely to hold distant sources accountable for effective functioning of applications, information obtained, privacy of interactions, etc. Therefore, the interaction design of cloud-based sources ought to take into account ways to mitigate concerns as well as convey the limits of responsibilities for cloud services and individuals accessing them. Ongoing work by our team is expanding on this experiment while simultaneously planning qualitative studies, including interviews with employees working in a smart office, in an effort to fully understand the relationship between clouds and human social responses to computing in this environment, so that we

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