THE INFLUENCE OF PRESSURE TO PERFORM AND EXPERIENCE ON CHANGING PERCEPTIONS AND USER PERFORMANCE: A MULTI-METHOD EXPERIMENTAL ANALYSIS

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1 THE INFLUENCE OF PRESSURE TO PERFORM AND EXPERIENCE ON CHANGING PERCEPTIONS AND USER PERFORMANCE: A MULTI-METHOD EXPERIMENTAL ANALYSIS Andreas Eckhardt Goethe University Frankfurt Grueneburgplatz 1; Frankfurt eckhardt@wiwi.uni-frankfurt.de Research-in-Progress Christian Maier University of Bamberg An der Weberei 5; Bamberg christian.maier@uni-bamberg.de Ricardo Buettner FOM University of Applied Sciences for Economics and Management Arnulfstraße 30; München ricardo.buettner@fom.de Abstract To address shortcomings of predominately subjective measures in empirical IS research on IT usage and human-computer interaction, this paper uses a multi-method experimental analysis extending empirical surveying with objective measures from eyetracking and electrodermal activity (EDA). In a three stage process, objective user performance is observed in terms of task fulfillment and user performing of participants in four focus groups, classified by user system experience and the treatment pressure to perform. Initial results of this research-in-progress reveal that users with prior system experience perform considerably better and faster than users without system experience. This also accounts for users under pressure to perform compared to users without pressure to perform. However, the results of the EDA show that users under pressure to perform also have a higher objective strain level. Furthermore, a first regression analysis outlines that objective performance might help to understand user s system satisfaction to a greater extent. Keywords: User Performance, Experimental Analysis, Eye-Tracking, Electrodermal Activity, Pressure to Perform, System Experience, Objective Data Thirty Third International Conference on Information Systems, Orlando

2 Human-Computer Interactions Introduction Imagine an organization, in which employees are subjected to different degrees of pressure to perform. The reason for this can be multifaceted so that some have to perform well because they are at the end of their probationary period and hope to be offered a regular employment contract. Others have a high variable salary component within their current contract, meaning that these employees experience pressure to perform to a higher degree than those with fixed salaries. In addition, employees corporate division determines the level of pressure to perform, so that output-oriented divisions such as investment banking or sales departments determine hard performance indicators for their employees, whereas back office employees for secondary business process are confronted less often with hard indicators. In summary, almost every employee experiences a different level of external pressure to perform. Recent Information Systems (IS) research assumes that the perceived level of pressure increases even further when an individual is embedded in a variety of technologies which are perceived as complex and which put employees under stress (Ayyagari et al. 2011; Tarafdar et al. 2010). Nonetheless, the results of prior IS research are limited concerning four issues. Firstly, no objective data for the level of strain are included in these approaches, although prior research has identified discrepancies between subjective and objective measurements of strain (Riedl et al. 2012). Secondly, pressure on system users is solely captured as the perceptual belief of employees but not in experimental settings (Ayyagari et al. 2011). Thirdly, the factor of experience is neglected within these articles, although prior work on human-computer interactions emphasizes the great importance of IS experience in general, as well as system experience, in accomplishing a distinct task or in using an IS effectively and efficiently (Mead et al. 2000; Kim and Malhotra 2005). Fourthly, no article investigates the consequences of pressure and strain on objective user performance. Hence, the link between technology usage under pressure to perform depending on prior system experience and objective user performance still remains unclear. The reason for this might be found in the predominance of purely empirical research, in which results are on the one hand not checked against the actual level of pressure to perform on the respective system user and, on the other hand, are limited because the determination of user performance and strain in real conditions requires experimental settings with objective measures (e.g., Ayyagari et al. 2011; Bhattacherjee and Premkumar 2004; Kim and Malhotra 2005). In order to overcome these shortcomings, we set up a multi-method experimental setting with three stages, including the use of eye-tracking technology, electrodermal activity (EDA), as well as both a pre- and a post-experimental survey. This multi-method experimental setting facilitates the answering of a wide range of recent research calls, as well as the verification of prior empirically based results. Among others, we can contribute to research on IS continuance usage by responding to the call from Bhattacherjee and Premkumar (2004) to include performance in expectation-disconfirmation theory. Furthermore, in the context of techno-stress related research, Ayyagari et al. (2011) call for research collecting data at two different points in time to confirm the causality of perceptions, for research using an experimental setting in order to isolate technologyinduced effects, and for research investigating the effect of predispositions as well as contextual variables. The separation into four focus groups enables statements to be made about whether individuals with pressure to perform fulfill the experimental tasks better than individuals without pressure to perform. In addition, the inclusion of EDA into our experimental settings enables the objective measuring of levels of strain, so that we extend stress-related IS research by not solely focusing on perceived levels of strain (Ahuja et al. 2007; Ayyagari et al. 2011). Besides, our research provides methodological insights by contributing to the upcoming stream of research using eye-tracking technology in psychology (van Hooft and Born forthcoming; Madera and Hebl forthcoming) and human-computer interaction (Cyr et al. 2009; Xu and Riedl 2011) that might reveal unprecedented insights into users behavior (Pieters et al. 2002). Furthermore, including EDA into research on human-computer interactions offers new opportunities to gather more objective data concerning the technology usage process, which might be significant for techno-stress related research. To address these shortcomings in prior IS-related research and to respond to these research calls, the remainder of this research-in-progress article is as follows. In the next section, we will provide an overview of relevant literature and make propositions for our research. Then, we explain our multimethod experimental setting in more detail. Before discussing possible future results for IS research, we 2 Thirty Third International Conference on Information Systems, Orlando 2012

3 Eckhardt et al. / The Influence of Pressure to Perform and Experience provide initial results based on a pre-study involving 17 participants. The entire experiment involving more than 150 participants will be completed by mid-december Research Background and Development for Propositions With our research, we tackle the newly emerging phenomenon that social network sites users more and more experience feelings of strain and fatigue (Gartner 2011; Maier et al. 2012). The theoretical foundation of this approach is the three-layered stress-strain-outcome (SSO) model (Koeske and Koeske 1993). The model reveals how stressors such as pressure to perform become noticeable in an individual s life. To explain this impact, the model makes use of the three variables stress, strain, and outcome. It suggests a linear direct influence of stressors on strain, while only the latter is actually a contributing factor for distinct outcome variables, such as user s satisfaction or continuance usage intention. The whole stress-strain-outcome model is among others transferred to the work context (Cheung and Tang 2010). It is suggested that work stress caused by job characteristics such as the perceptions of service culture or pressure to perform have an influence on strain in the form of somatic complaints or psychological health. Discussing the impact of pressure to perform on individual performance, Parkinson s Law (Parkinson 1957) assumes that pressure to perform is a source of higher performance. Both contradictory views are brought into accordance by a U-shaped relation between pressure and performance. Beside Parkinson s Law, early empirical work by Wendt (1955) concludes that external pressure to perform increases performance. This is confirmed by social facilitation research (e.g., Zajonc 1965) that suggests for simple tasks, evaluation pressure may actually lead to better performance than when such pressure is absent. Analyses in learning goal conditions also emphasize that moderate levels of pressure to perform are more effective than low levels of pressure in facilitating performance (Utman 1997). Interestingly, while being part of a major research stream in management and psychology research, in IS research, very few studies examine the influence of pressure to perform on performance (Hwang 1994). Based on work by Hwang (1994), who develops a general model for decision making under pressure to perform in terms of time pressure for IS research, Marsden et al. (2002a, 2002b, 2006) observe decisionmaking under time pressure with different information sources and performance-based financial incentives. Additionally, Mallat et al. (2006) assume that time pressure, for example, in buying a ticket for public transportation, is a significant determinant for intention to use mobile ticketing. All of these approaches have in common that they evaluate user performance either as perceptual measure of the user itself or any third person involved. Only a few approaches use objective measurements such as log files data to evaluate performance in terms of task fulfillment. Compared to this there is even less information about how users perform in fulfilling these tasks. This is due to the fact that information relating to this case could only be gathered using complex controlled laboratory experiment settings. Within this research we focus solely on the aspect of task fulfillment. Summing up, increasing pressure to perform might indeed lead to high performance; however it should be kept in mind, as outlined by Bhagat (1982), that such a push towards performance could also lead to lower levels of job satisfaction and thus higher levels of strain. Hence, within our multi-method experimental setting of human-computer interaction, we combine both perspectives by assuming the following: Proposition 1: Users with high pressure to perform have a significantly better user performance than users without pressure to perform. Proposition 2: Users with high pressure to perform have a higher objective level of strain than users without pressure to perform. The relationship between user performance and system experience is one of the most-researched topics in the field of human-computer interaction (Tullis and Albert 2008). Throughout the last thirty years, several research approaches with varying methodologies investigate the impact of general information technology (IT), computer experience, or specific system experience on the performance of users of different age, gender, occupation, or level of education. In general all approaches share the acknowledgement that higher experience consequently leads to better user performance. For example Mead et al. (2000) show using two experiments examining the effects of general computer experience and age on library system search performance that for both the groups observed prior computer experience is Thirty Third International Conference on Information Systems, Orlando

4 Human-Computer Interactions positively associated with better user performance (Whiteside et al. 1985; Mead et al. 2000). In contrast to prior approaches in human-computer interaction, system experience is seldom used in IT adoption or stress-related research to explain user performance. To capture user experience in all its nuances and for all related tasks in the experiment, we develop several task-related system experience measures (see Table 4 in the appendix) in order to observe the following: Proposition 3: A high level of prior system experience for a distinct task is always positively associated with better objective user performance for the distinct task. In addition to the SSO model by Koeske and Koeske (1993), expectation-disconfirmation theory (EDT) is the second approach underlying this research. EDT is perfectly suitable for this research setting as it explains the change of users attitudes and beliefs from the pre- to the post-usage phase of a certain technology and the resulting user satisfaction (e.g., Bhattacherjee and Premkumar 2004). In these approaches, the creation of satisfaction is explained based on initial expectations in a pre-usage phase, the usage of a technology itself, and the comparison of initial expectations with post-usage perceptions in terms of disconfirmations. Here, initial expectations are perceptions about the performance in terms of usefulness or ease of use of a technology before using it, whereas post-usage perceptions include perceptions about the performance again in terms of usefulness or ease of use after using the technology. Disconfirmations are subjective post-usage comparisons and hence reflect whether the performance was better, the same as, or worse than expected. In line with EDT, the perceptions in terms of expectation, disconfirmation, and performance influence user satisfaction. Moreover, expectations have a direct influence on disconfirmation and satisfaction and a mediating one through performance. Proposition 4: User performance mediates the influence of the initial expectations on disconfirmation and satisfaction. Nevertheless, the complete model with all its six EDT relationships is researched solely once (Lankton and McKnight 2012, p. 112) because performance is often not included in the model. Nonetheless, all approaches share the recognition that performance is captured in terms of subjective post-usage perceptions, whereby this research captures performance with objective data in terms of task fulfillment and user performing (number of cognitions and AOI fixations) with an experimental setting. Compared to the solely perceptive measures for user performance, we propose that the relations of EDT can be better validated using objective data, so that the explanatory power is higher for research models using objective user performance as a mediator in EDT. Therefore, we assume the following: Proposition 5: Compared to perceptual measures, objective user performance explains the influence of the initial expectations on disconfirmations and satisfactions in EDT to a greater extent. The increasing use of IT in individuals private and organizational lives is seen by academic literature and the popular press as a significant source of increasing perceptions of stress (Ayyagari et al. 2011). Academic literature calls this phenomenon technostress and examines which creators are the sources of stress experienced by individuals while using IT (Ragu-Nathan et al. 2008). In this context, technooverload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty (Ragu-Nathan et al. 2008; Tarafdar et al. 2010) are identified as stressors, which result in perceptions of strain (Ayyagari et al. 2011) and influence satisfaction and performance in a negative manner (Tarafdar et al. 2010). Despite these significant insights, the results of these approaches are limited because of the sole use of subjective measures of performance and strain as well as the use of single-point empirical data. As a consequence of these limitations, Ayyagari et al. (2011 p. A8) call for research using experimental settings to overcome these shortcomings. While determining performance with objective data from an experiment, perceptions of strain with EDA data, and the execution of two empirical surveys, we overcome these shortcomings but confirm the proposed hypotheses of prior research (Ayyagari et al. 2011; Tarafdar et al. 2010) and suggest the following: Proposition 6: The higher the perceived techno-stress, the lower the objective user performance. Proposition 7: The higher the perceived techno-stress, the higher the subjective and objective levels of strain. Research results will be checked with regards to individual differences in terms of demographics and personality traits. Age, gender, and work experience are used as examples of participants demographics to delineate our results, as prior research identifies the importance of these differences (e.g., Venkatesh et 4 Thirty Third International Conference on Information Systems, Orlando 2012

5 Eckhardt et al. / The Influence of Pressure to Perform and Experience al. 2003). Individuals personality traits, which depict constant patterns of thoughts, feelings, and behavior across diverse situations that distinguish individuals from each other (McCrae and Costa 2006), also represent a part of the empirical survey within the experiment because prior research identifies a link between personality and performance (Barrick and Mount 1991; Rasch and Tosi 1992). Therefore, the Five-Factor Model and cognitive rigidity represent traits on distinct hierarchical levels which the result takes into account. Moreover, prior research identifies additional demographics such as gender, which show, for instance, that males look at results provided by web searches, in a more linear manner than females (Lorigo et al. 2006). Methodology The multi-method approach of this article consists of an experiment using eye-tracker technology and two surveys as well as EDA using the following process. First, each test participant is welcomed by the experimenter (supervisor of the experiment). Afterwards, the participant fills in a two-sided questionnaire in stage 1. In stage 2, the supervisor turns the task-sheet, including a short note of each task that has to be fulfilled, over to the participant and reads all three tasks out aloud. In the following, the participant has time to read each task again and to ask questions. In stage 3, we take the necessary precautions for the experiment, for which we make use of an eye-tracker and EDA device. Hence, the eye-tracker is calibrated and the EDA electrodes are attached. In stage 4, the experiment starts with the first task that the participant has to accomplish. When the participant thinks that the task is completed successfully, she clicks on a button marked task completed and the second task becomes visible on the monitor screen. The same procedure is then repeated again until all three tasks are processed. After the experiment, the second survey is presented to the participant in stage 5, which has to be completed at the end. On average, the whole experiment takes about 45 minutes and each participant receives an expense allowance of 10 Euros. Tasks and Treatment of Focus Groups As most of the participants are part-time MBA or Bachelor students, from subjects such as IS and business management, we intend to present them realistic tasks relating to their real-lives. As a majority of these students will leave their current employer after graduation or is at least hopeful of doing so, we choose IT-based tasks related to the students application and enterprise contacting process. Therefore, we use the professional career network LinkedIn.com and set the participants of our experiment three tasks of a different level of difficulty, which have to be fulfilled in the same order. These tasks are: 1. Ask your contact Christian Maier for a letter of recommendation. 2. Apply to Oracle for a new job of your choice. 3. Identify and name the Global Head of Recruiting at BMW. As a standard feature of the experimental settings, we set up a special treatment for half of the participants. Therefore, we encourage these participants to perform to the best of their abilities during the experiment. We apply forms of pressure by providing two different task descriptions for the participants with and without pressure to perform, and by arguing that they have to accomplish the tasks as well and as quickly as possible. In addition, we tell them that after the experiment all participants will be ranked in relation to their results in the experiment. To the other half of the participants, we tell them that they should relax while solving the three tasks and that we do not limit the time they need or determine how many tasks they process. As a second variable for classifying our focus groups we choose the participants prior system experience with LinkedIn.com. Concerning system experience, we ask participants within the pre-experimental survey, if they currently have or have had a profile in LinkedIn.com in the past. In the end, we come up with four different focus groups. The first group has no system experience and is not pressured to perform. The second group has system experience but is not pressured to perform. The third and the fourth group are both pressured to perform, with only the fourth group having system experience with LinkedIn.com (see Figure 1). Thirty Third International Conference on Information Systems, Orlando

6 Human-Computer Interactions Survey The survey consists of two chronologically separated parts; one before and one after the experiment. Within the first survey the following elements are taken account of: demographics, general IT knowledge, expectations concerning the usefulness, ease of use, and complexity, usage intentions, attitudes, subjective norms, IT-related feelings of strain, and control variables in terms of negative affect and the Big Five personality traits (McCrae and Costa 2006). The second survey includes measurements to capture the subjective performance and disconfirmations for usefulness and ease of use. Moreover, subjective norms, continuance usage intentions, and satisfaction with the system as well as satisfaction with the participants own performance in the experiment are included. Moreover, we again take account of strain variables and attitudes. In addition, the techno-stressors complexity and overload, as well as the control variable cognitive rigidity, are accounted for in the post-experiment survey. Eye-Tracking Eyes are the sole means of processing visualized and written information from external sources, but not every point or source of information that is captured with our eyes is also cognitively processed. Whether information is processed or not depends significantly on how long it is fixed upon by an individual s eyes. Hence, recent research differs between fixations on specific regions of interest, in which the eyes remain motionless, and saccades, which describe situations in which eyes move rapidly (Rayner 1998). During saccades, in which eyes move from one fixation to another one and last between 15 and 40 milliseconds (ms) (Reichle et al. 1998), individuals do not process information because of the rapid eye movements (Rayner 1998). In contrast, information is processed in fixations, which last on average about 225 ms but are 50 ms for an average reader (Liversedge et al. 2004; Rayner et al. 1981; Lankford 2000). For this research, eye-tracking is performed using the double Eyegaze Edge TM System eye-tracker paired with a 19 LCD monitor (86 dpi) set at a resolution of 1280x1024, whereby the eye-tracker samples the position of participants eyes at the rate of 60Hz. The eye-tracker is installed under the monitor and tracks the participant s eyes during the entire stage 4. In order to track fixations, we divide the websites into Areas of Interest (AOI), which include, among others, the available functions of LinkedIn.com on the taskbar and which are of importance for accomplishing the three tasks. Based on these AOI, we derive fixations that last in accordance with the definition at least 50 ms. Research articles using eye-tracking method are less common (Cyr et al. 2009) despite the historical importance of eye movements. Nonetheless, psychological research uses this method to detect dissemblance behavior (van Hooft and Born forthcoming) or the discrimination of stigmatized applicants within the application process (Madera and Hebl forthcoming). In the context of IS research, eye-tracking is mostly used in the context of human computer interaction to visualize the number of fixations on pages including search results (Cutrell and Guan 2007) and the design of websites (Cyr et al. 2009). More recently, a few other articles make use of eye-tracking, such as Xu and Riedl (2011), who describe an approach using eye-tracking data to understand individuals choice of online payment. Electrodermal Activity (EDA) EDA indicates activities in the sympathetic division of the autonomic nervous system and hence provides information concerning alternating influences of strain (Andreassi 2007). Here, recent research presents increasing electrodermal activity when stressors arise (Setz et al. 2010; Svetlak et al. 2010). Prior research suggests that typical values range between 2 and 20 µs (micro siemens) (Dawnson et al. 1990), whereby Schmidt and Walach (2000) provide an overview of articles using EDA in the context of psychological purposes. In order to capture these objective strain values, we use the MentalBioScreen K3 device, which traces participants EDA value each second. Therefore, two sensors (ECG electrodes type 10-W 55 GS) are applied to the non-dominant hand of the participant (1 channel measurement). 6 Thirty Third International Conference on Information Systems, Orlando 2012

7 Eckhardt et al. / The Influence of Pressure to Perform and Experience Key indicators derived from the multi-method approach Based on the three-step approach, we receive subjective and objective data, which can be used to examine an array of different relationships. The two empirical surveys provide us a wide range of subjective data, such as perceptual beliefs in terms of pre-usage expectations or post-usage confirmations. The usage of EDA presents objective data concerning the strain level of participants during the whole experiment as well as the situations in which participants experience more strain. Moreover, the experimental design provides the possibility of tracking the number of tasks which have been completed successfully. The use of eye-tracker provides additional key indicators for determining users performing in an objective manner. First, the software stops the time needed for completing the three tasks. Second, the number of mouse clicks is stored by the software. Here, we assume that this indicator is effective in determining the performance when relating this to the number of successfully completed tasks, so that fewer clicks indicate better performance. Third, we define areas of interest (AOI), which include the functions on the taskbar that are of importance for accomplishing the tasks successfully. We assume that the longer the time taken for these AOIs to be captured by the participant s eyes as well as the higher the number of views of the AOIs, the better the performance. The entire research design with the three stages of our multi-method experimental analysis is depicted in Figure 1. Figure 1. Experimental Design First Research Results So far 17 participants have taken part in our experiment. All are male, between 22 and 32 years old (mean years), and enrolled as part-time students in IS at our university. All participants have work experience between 0.5 and 12 years (mean 6.96 years). Based on the results of the pre- and post-survey, the eye-tracking, and the EDA measurement of these 17 participants, we provide some initial results concerning our propositions. First of all, we compare the performance of the focus group with pressure to perform to the focus group without pressure to perform. For this purpose, we only examine the performance in terms of number of tasks, which have been completed successfully (task fulfillment), the overall time needed, as well as the ratio of the time needed to complete one task successfully (user performing). Table 1 indicates that individuals who were pressured by us perform better. This group of individuals completed more tasks, required less time, and hence completed two tasks successfully in the same time that individuals without pressure to perform completed only one task. It is also outlined by the help of the eye-trackers that participants with pressure to perform view the AOI (here: the navigation-bar) more intensively and frequently and thus perform better. Heat maps in the appendix also visualize that participants without pressure to perform view the AOI with less focus than participants with pressure to perform. Thirty Third International Conference on Information Systems, Orlando

8 Human-Computer Interactions Table 1. Impact of Pressure to Perform on Task Fulfillment and User Performing Participants with pressure to perform Participants without pressure to perform Successfully com pleted tasks (m ean) T im e needed for all tasks (m ean) seconds seconds Com pleted a task successfully ev ery seconds seconds Ratio, to which navigation-bar is v iewed % 26.38% Num ber of views on nav igation-bar In a second step, we separate our data set regarding the focus groups with system experience and without system experience in order to determine the association between prior system experience and user performance. As only individuals with overall few and no prior using experience of LinkedIn participated in the experiment, we are not able to distinguish for the criterion of system experience per individual task. Again, we compare the tasks, which have been completed successfully, the overall time needed, and the average time needed to complete one task successfully. Initial results are presented and indicate that individuals with system experience perform much better. Table 2. Impact of System Experience on User Performance Participants with System Experience Participants without System Experience Successfully com pleted tasks (m ean) T im e needed for all tasks (m ean) seconds seconds Com pleted a task successfully ev ery seconds seconds By separating individuals depending on whether they are in the focus group with or without pressure to perform, we investigate the influence of pressure to perform on strain. Here, we show using objective data from the EDA that individuals with pressure to perform (16 µs) are strained to a higher degree than individuals without pressure to perform (14 µs). In addition, we investigate the progress of the objective strain levels of the users in our experiment with and without pressure to perform, while accomplishing the tasks. Here, we show that individuals from the focus group with pressure to perform have three significant elevations after reading and starting to process each task (Figure 2). In contrast, the level of strain does not follow a predefined progress for individuals of the focus group without pressure to perform (Figure 2) Figure 2. Progress of objective strain in µs for participants without (left) and with pressure to perform (right) One possibility for contributing to EDT is the inclusion of objective performance measures into the complete EDT, which is recently investigated by Lankton and McKnight (2012) in relation to perceptions. While, objective user performance is measured as task fulfillment and user performing as described above, expectation, disconfirmation, and satisfaction are operationalized as in the underlying work by Bhattacherjee and Premkumar (2004). As this is part of an ongoing study and still research-in-progress, our data sample is relatively small, so that we could not make use of structural equations modeling. Hence, we perform a regression analysis. The results suggest that the three factors of expectation, disconfirmation, and objective performance explain 75.2 percent of user satisfaction, whereby expectation 8 Thirty Third International Conference on Information Systems, Orlando 2012

9 Eckhardt et al. / The Influence of Pressure to Perform and Experience and performance are the only two significant influencing factors. Table 3. Results of the Regression Analysis Dependent Variable Variable Mean Deviation B β 95% lower 95% upper R² Satisfaction constant 4.416** Expectation ** 0.523** Objective performance ** 0.637** Disconfirmation Note: satisfaction is measured on a 7-point Likert scale (1 = totally disagree; 7 = totally agree); disconfirmation is measured with a 7-point semantic differential (1 = much worse; 7 = much better); expectation is measured on a 5-point Likert scale (1 = totally disagree; 5 = totally agree); performance is measured with objective data with the reverse logarithmic of the overall time needed multiplied with the number of successfully fulfilled tasks (-> the higher the better) * p < 0.1; ** p < % Conclusion and Next steps The first results of our multi-method experimental analysis are promising concerning our underlying propositions. We show among other findings that pressure to perform and system experience lead to higher user performance, while pressure to perform also enhance users objective level of strain. Based on these results, we intend to increase the number of participants up to 150 during the third and fourth quarters of Based on the resulting data, we plan to compare, on the one hand, performance indicators and strain values of the four focus groups. On the other hand, we intend to use objective key indicators from eye-tracking and EDA in order to overcome shortcomings in the predominantly empirical prior research on individual technology usage and user performance with purely subjective data based on users perceptions. Hence, the use of structural equations modeling (PLS or AMOS) is intended, in which we combine subjective and objective data. Among other issues, we will investigate the influence of subjective and objective stressors on objective strain values derived from EDA. Furthermore, objective key indicators from the eye-tracking software should be integrated into EDT to explain users belief updates towards a distinct IS. In summary, this data-set enables us to overcome some short-comings of purely empirical research by using a multi-method experimental setting with subjective and objective data (e.g., Ayyagari et al. 2011; Bhattacherjee and Premkumar 2004; Kim and Malhotra 2005). Moreover, this setting is a response to many current research calls (Bhattacherjee and Premkumar 2004), and offers new insights by adopting new methodologies (Pieters et al. 2002; Riedl et al. 2012), by using data from empirical surveys, eye-tracking, and EDA, all within one line of research. References Ahuja, M. K., Chudoba, K. M., Kacmar, C. J., McKnight, H. D., and George, J. F IT Road Warriors: Balancing work-family conflict, job autonomy, and work overload to mitigate turnover intentions, MIS Quarterly (31:1), pp Andreassi, J. L Psychophysiology: Human behavior and physiological response, Mahwah, N.J: Lawrence Erlbaum. Ayyagari, R., Grover, V., and Purvis, R Technostress: Technological Antecedents and Implications, MIS Quarterly (35:4), pp Barrick, M. R., and Mount, M. K The Big Five Personality Dimensions and Job Performance: A Meta-Analysis, Personnel Psychology (44:1), pp Bhagat, R Conditions under which stronger job performance-job satisfaction relationships may be observed: A closer look at two situational contingencies, Academy of Management Journal (25), pp Bhattacherjee, A., and Premkumar, G Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test, MIS Quarterly (28:2), pp Cheung, F., and Tang, C The Influence of Emotional Dissonance on Subjective Health and Job Satisfaction, Journal of Applied Social Psychology (40:12), pp Cutrell, E., and Guan, Z What are you looking for?: an eye-tracking study of information usage in web search, Proceeding CHI '07 Proceedings of the SIGCHI conference on Human factors in computing systems. Thirty Third International Conference on Information Systems, Orlando

10 Human-Computer Interactions Cyr, D., Head, M., Larios, H., and Pan, B Exploring Human Images in Website Design: A Multi- Method Approach, MIS Quarterly (33:3), pp Dawnson, M., Schell, A., and Filion, D The electrodermal system, in Principles of psychophysiology: Physical, social, and inferential elements, J. T. Cacioppo, and L. G. Tassinary (eds.), Cambridge [England] ;, New York: Cambridge University Press. Gartner Gartner Survey Highlights Consumer Fatigue with Social Media. Accessed 7 September Hwang, M Decision making under time pressure: a model for information systems research, Information & Management (27:4), pp Kim, S. S., and Malhotra, N. K A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena, Management Science (51:5), pp Koeske, G. F., and Koeske, R A Preliminary Test of a Stress-Strain-Outcome Model for Reconceptualizing the Burnout Phenomenon, Journal of Social Service Research (17:3-4), pp Lankford, C Gazetracker: Software Designed to Facilitate Eye Movement Analysis, Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp Lankton, N. K., and McKnight, H. D Examining Two Expectation Disconfirmation Theory Models: Assimilation and Asymmetry Effects, Journal of the Association for Information Systems (13:2), pp Liversedge, S. P., Rayner, K., White, S. J., Vergilino-Perez, D., Findlay, J., and Kentridge, R Eye movements when reading disappearing text: Is there a gap effect in reading? Vision Research (44:10), pp Lorigo, L., Pan, B., Joachims, T., Granka, L., and Gay, G The influence of task and gender on search and evaluation behavior using Google, Information Processing & Management (42:4), pp Madera, J. M., and Hebl, M. R. forthcoming. Discrimination against facially stigmatized applicants in interviews: An eye-tracking and face-to-face investigation, Journal of Applied Psychology. Maier, C., Laumer, S., Eckhardt, A., and Weitzel, T "When social networking turns to social overload: Explaining the stress, emotional exhaustion, and quitting behavior from social network sites' user, " Proceedings of the European Conference on Information Systems (ECIS 2012), Paper 71. Mallat, N., Rossi, M., Tuunainen, V. K., and Oorni, A The impact of use situation and mobility on the acceptance of mobile ticketing service, Proceedings of the 39th Annual Hawaii International Conference on System Sciences. Marsden, J. R., Pakath, R., and Wibowo, K. 2002a. Decision making under time pressure with different information sources and performance-based financial incentives: Part 1, Decision Support Systems (34:1), pp Marsden, J. R., Pakath, R., and Wibowo, K. 2002b. Decision making under time pressure with different information sources and performance-based financial incentives: Part 2, Decision Support Systems (34:1), pp Marsden, J. R., Pakath, R., and Wibowo, K Decision making under time pressure with different information sources & performance based financial incentives: Part 3, Decision Support Systems (42:1), pp McCrae, R. R., and Costa, P. T Personality in adulthood: A five-factor theory perspective, New York NY: Guilford Press. Mead, S. E., Sit, R. A., Rogers, W. A., Jamieson, B. A., and Rousseau, G. K Influence of general computer experience and age on library database search performance, Behaviour and Information Technology (19:2), pp Oliver, R. L Satisfaction: A behavioral perspective on the consumer, New York: McGraw Hill. Parkinson, C. N Parkinson's law: And other studies in administration, Cutchogue, NY: Buccaneer Books. Pieters, R., Warlop, L., and Wedel, M Breaking Through the Clutter: Benefits of Advertisement Originality and Familiarity for Brand Attention and Memory, Management Science (48:6), pp Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., and Qiang, T The Consequences of Technostress for End Users in Organizations: Conceptual Development and Empirical Validation, Information Systems Research (1:4), pp Rasch, R., and Tosi, H Factors affecting software developers performance: An integrated 10 Thirty Third International Conference on Information Systems, Orlando 2012

11 Eckhardt et al. / The Influence of Pressure to Perform and Experience approach, MIS Quarterly (16:3), pp Rayner, K Eye movements in reading and information processing: 20 years of research., Psychological Bulletin (124), pp Rayner, K., Inhoff, A. W., Morrison, R. E., Slowiaczek, M. L., and Bertera, J. H Masking of foveal and parafoveal vision during eye fixations in reading, Journal of Experimental Psychology: Human Perception and Performance (7:1), pp Reichle, E. D., Pollatsek, A., and Fisher, D Toward a model of eye movement control in reading, Psychological Review (105:1), pp Riedl, R., Kindermann, H., Auinger, A., and Javor, A Technostress from a Neurobiological Perspective - System Breakdown Increases the Stress Hormone Cortisol in Computer Users, Business & Information Systems Engineering (4:2), pp Schmidt, S., and Walach, H Electrodermal Activity - State-Of-The-Art Measurement And Techniques For Parapsychological Purposes - EDA, Journal of Parapsychology (64:2), pp Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tröster, G., and Ehlert, U Discriminating stress from cognitive load using a wearable EDA device., IEEE Transactions on Information Technology in Biomedicine (14:2), pp Svetlak, M., Bob, P., Cernik, M., and Kukleta, M Electrodermal complexity during the Stroop colour word test, Autonomic Neuroscience (152:1-2), pp Tarafdar, M., Tu, Q., and Ragu-Nathan, T. S Impact of Technostress on End-User Satisfaction and Performance, Journal of Management Information Systems (27:3), pp Tse, D. K., Nicosia, M., and Wilton, P Consumer satisfaction as a process, Psychology & Marketing (7:3), pp Tullis T, and Albert W Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, San Francisco: Morgan Kaufmann; Utman, C Performance effects of motivational state: A meta-analysis, Personality and Social Psychology Review (1:2), pp van Hooft, E. A. J., and Born, M. forthcoming. Intentional response distortion on personality tests: Using eye-tracking to understand response processes when faking, Journal of Applied Psychology. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D User Acceptance of Information Technology: Toward a unified View, MIS Quarterly (27:3), pp Wendt, H Motivation, effort, and performance, in Studies in motivation, D. McClelland (ed.), New York: Appleton-Century-Crofts, pp Whiteside, J., Jones, S., Levy, P.S. and Wixon, D "User Performance With Command, Menu, and Iconic Interfaces," CHI '85 Proceedings, San Francisco, April 14-18, ACM, New York, NY, pp Xu, Q., and Riedl, R Understanding Online Payment Method Choice: An Eye-tracking Study, ICIS 2011 Proceedings. Paper 18. Zajonc, R. B Social facilitation, Science (149), No. 3681, pp Appendix Figure 3. Heat maps for participants without pressure to perform (left) and with pressure to perform (right) Thirty Third International Conference on Information Systems, Orlando

12 Human-Computer Interactions Table 4. Operationalization of system experience per task Construct operationalization Scale Source Task-related user experience I use career networks I use LinkedIn I use the fee-based premium service in LinkedIn I have already applied for a new position via LinkedIn I add new contacts in LinkedIn I search for information about companies/persons in Linkedin Very frequently never Very frequently never Very frequently never Very frequently never Very frequently never Very frequently never Self-developed 12 Thirty Third International Conference on Information Systems, Orlando 2012