دانلود مقاله ISI انگلیسی شماره 7446
عنوان فارسی مقاله

ارزش گروه و قصد استفاده - مطالعه سیستم های اطلاعاتی مدیریت فاجعه چند سازمانی برای امنیت عمومی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7446 2011 11 صفحه PDF سفارش دهید 10250 کلمه
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عنوان انگلیسی
Group value and intention to use — A study of multi-agency disaster management information systems for public safety
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 50, Issue 2, January 2011, Pages 404–414

کلمات کلیدی
- مدیریت بحران - بخش عمومی - موفقیت سیستم های اطلاعاتی - قصد استفاده - ارزش گروه - رضایت کاربر - وظیفه پشتیبانی -
پیش نمایش مقاله
پیش نمایش مقاله ارزش گروه و قصد استفاده - مطالعه سیستم های اطلاعاتی مدیریت فاجعه چند سازمانی برای امنیت عمومی

چکیده انگلیسی

This paper examines and extends the theory of information systems success in the context of large-scale disaster management (DM) for public safety. In the recent past, various evaluation reports on DM efforts have concluded that information quality and system quality are major hurdles for efficient and effective multi-agency DM and are critical antecedents for information systems (IS) success. In contrast to the wealth of literature on IS success in profit-oriented business environments, research regarding drivers of public sector IS success is scarce. This research develops and empirically tests a model that explains IS usage intention as a reflective measure of IS success in the public sector DM domain. In this paper, the effects of the expected value of IS for the entire group of collaborating DM agencies, task support, user satisfaction, and three specific information/service quality dimensions on usage intention are examined. Data was collected from emergency responders using a questionnaire survey method during multi-agency, cross-national DM exercises at the Dutch–German border. The results of the data analysis revealed that expected group value is a key determinant of intention to use in the public sector DM domain. The data analysis also showed that perceived task support only has an indirect effect, through user satisfaction, on the usage intention. These findings imply that previously suggested IS success models for business environments are likely to fall short in their explanatory power and applicability for highly volatile complex disaster environments that require immediate coordinated responses from a large number of organizations. Possible directions for future research are also discussed along with other findings and implications.

مقدمه انگلیسی

The use and performance of communication and decision support systems in disaster management (DM) have attracted the attention of researchers for over two decades [52]. However, it is only recently that researchers have started to emphasize decision support and information sharing across different DM agencies as well as the quality of the shared information. The main reason for the current emerging interest may lie in the fact that evaluation reports on some recent disasters, such as 9/11 [20], the SARS outbreak in Asia [45] and the fire in the Schiphol Retention Complex [62], have revealed that poor information quality hampered the efficiency and effectiveness of interagency disaster response activities. For example, during the response to the 9/11 attacks fire fighters were not able to receive the information about the WTC towers that police had [20]. Information about the structure of the WTC towers only became available to DM organizations several days after the disaster [20]. A common problem is the fact that although most of the data objects are available somewhere in the complex network, the processing of the data objects into relevant information accessible to the right person at the right time is missing. Response to disasters, whether natural (e.g., floods, earthquakes) or human induced (e.g., terrorist attacks), is a complex process [11] that involves severe time pressure [51], high uncertainty [4] and many stakeholders [15], which results in unpredictable information needs [38]. In other words, DM operations are information-intensive activities [17] and [21]. However, most disaster responders have to cope with incomplete, unavailable and/or outdated information when a disaster strikes. Access to quality information is essential for DM agencies to decide and act under demanding conditions [31] since poor information quality can be lethal for both emergency responders and victims [27] and [58]. Accordingly, decision-making pertaining to the hazards of a disaster relies heavily on effective information sharing and coordination between a large number of autonomous actors including various DM agencies, private organizations, and civilians [9]. The challenges for individual or group decision support systems in disaster response situations are both diverse and massive. The people who must work together have no history of doing so, they have not developed a trust or understanding of one another's abilities, and the totality of resources they each bring to bear have never before been exercised [61]. There is a wealth of literature on information quality and information systems (IS) success in the profit-oriented business environment. However, research on the drivers of IS success in the public sector where attaining the public good is often an important goal is relatively scarce. One exception is the work done by Fisher and Kingma [27], which underlines the criticality of IQ for DM. Public goods, such as public safety that this study focuses on, are delivered for the welfare of people (instead of for profit), ideally in a non-rivaled and non-exclusive manner for all societal groups. In disaster response situations characterized by highly volatile and chaotic environments, decision makers are forced to make swift decisions based on limited and/or incomplete information. This can put them under an extreme amount of pressure due to the immediate and potentially catastrophic consequences for making the wrong decision. Therefore, previously developed models of IS usage and success in a business environment are likely to fall short in their explanatory power and applicability in public domains in general and DM in particular. Another gap in the IS literature is that testable hypotheses and empirical (especially quantitative) studies are relatively scarce in the DM domain. One practical reason for this may be the fact that it is difficult to collect empirical data during a real disaster since the situation is unforeseeable, dangerous, and may prohibit researchers from approaching the disaster site. Furthermore multiple contexts, events, scope, control and time related problems make it difficult to collect data in disaster field studies [36]. In order to close the gap in the understanding of IS success this research develops and empirically tests a model of IS success for the DM domain in the public sector. Following a recent study in the IS success area [65], we adopt emergency responders' IS usage intention as a proxy measure of IS success. We examine the effects of expected value of the IS for the whole group of collaborating DM agencies, task support, user satisfaction, and three specific information/systems quality dimensions on the usage intention. For the empirical testing of our model, we analyze questionnaire survey data collected from professional emergency responders during a series of multi-agency, international DM exercises at the Dutch–German border. This research is geared toward improvement of IS design for public sector applications. In the process of achieving this objective the study also enhanced our understanding of IS success in the multi-agency DM context. The contribution of this paper is twofold. First, the study extends previous IS success models by theorizing and empirically validating the effects of expected group value (as opposed to the value to an individual organization). With expected group value, the extended model can explain why some IS' don't work in public sector applications while comparably designed IS' work fine in private sector applications. The second contribution of the study comes from the examination of relevant information quality (IQ) and systems quality (SQ) attributes expected to play important roles in the DM domain. This domain-specific knowledge provides the researchers in the DM domain with an important empirical baseline for future research in addition to being used as a design guideline by practitioners (e.g., IS designer/developer) in the domain. The analytical framework and findings can also offer useful insights into other domains with similar characteristics such as high pressure, multiple stakeholders, uncertainty, unpredictable information needs, and orientation toward public services. In the following section the theoretical foundation of our model of disaster management information systems (DMIS) success is presented. The research design and method of the study are explained in a subsequent section followed by the results of the data analysis. The paper concludes with a discussion about the implications of the findings and directions for future research.

نتیجه گیری انگلیسی

This study exemplifies the distinctive climate in the public sector DM domain and bears important implications for IS success research. The results of the data analysis revealed that expected group value is a key determinant of intention to use in the public sector DM domain. On the other hand, perceived task support only has an indirect effect, through user satisfaction, on the usage intention. This means that user satisfaction fully mediates the influence of perceived task support. Since expected group value also has a significant effect on user satisfaction, perceived task support may not play a decisive role by itself in IS success in the DM domain. This finding calls for a wider scope that goes beyond the personal or organizational boundaries dominating most private sector IS research. It also calls for recognition of IS users' concerns for group value, which requires a different design and incentive mechanism for public sector IS to support multi-agency collaboration. Previously suggested models of IS success, including the revised DeLone and McLean model [24], fall short in explaining IS success in some public sector domains and should be extended to take into account the group-level value (public good) of interconnected IS. A postmortem analysis of the 9/11 response also emphasized that “public service clearly emerged as a community value, not just a government function” ([20], p. 60) and illustrated instances where some organizational processes (e.g., procurement procedures) were ignored in an effort to serve the public quickly and compassionately. The four endogenous variables on the right side of the model (i.e., perceived task support, expected group value, user satisfaction, and intention to use) can all be considered as indicators of IS success. However, the analysis results strengthen our argument that a system that supports an individual user's tasks very well may not necessarily support multi-agency collaboration for group goals (e.g., the non-significant link from perceived task support to expected group value). Furthermore, the relationships around user satisfaction suggest that individual users' satisfaction, which may be determined by both perceived task support and expected group value, does not fully mediate the influence of expected group value on intention to use. Thus, in the public sector, an expectation of public good may exert a stronger direct effect on the usage intention than users' satisfaction. It is also recommended that public sector IS researchers and practitioners either include multiple IS success measures or consciously select a measure that best suits the purpose of the research. The IQ and SQ dimensions explain perceived task support relatively well compared to expected group value. It is understandable because they have long been developed and tested together in the private sector. The strong effect of the relevance dimension reiterates the serious concerns about information overload and distractions during the volatile disaster response phase. Regarding the determinants of expected group value, the two IQ/SQ dimensions explained more than 20% of the variance in expected group value, even when the effect of perceived task support was excluded.5 However, their effects were statistically non-significant, which might have been caused by the small sample size. Therefore, there exists an urgent need to retest these hypotheses (H4a and H5a) using a larger sample. In addition there needs to be an effort to identify other determinants of expected group value, given the strong positive effects of the expectation on intention to use. Non-technical factors, such as understanding of other agencies' roles, cross-organizational business/operation processes, and political conflicts, may be good issues to explore. Dawes et al. [20] points out that most service organizations (e.g., police, fire) have specialized structures, policies, processes, and incentives designed exclusively for each agency. This has contributed to the long-standing organizational and policy barriers to cross-agency information sharing and coordination. Therefore, technology itself cannot be a sufficient condition to solve the underlying organizational and data quality problems [20]. Practitioners in the DM area should also pay close attention to ways to leverage IS to improve their group-level DM performance. Based on the domain-specific findings, we argue that multi-agency DM systems should include roles and capabilities that can validate, filter, enrich and selectively forward information that is specifically for the right person at the right time in order to better support the various tasks that emergency responders carry out. “Getting the right kind of information into the hands of first responders in a form that they trust and could use” ([20], p. 63) has been a major challenge. Unfortunately, policy makers and DM leaders often lack a sophisticated understanding of ICT capabilities and limitations, which may lead to the wrong assumption that new ICTs will solve all their problems [20]. IS designers of multi-agency DMIS need to be able to recognize these challenges and limitations. In this way they will be able to implement information gateways and human-computer interfaces (HCI) that can assure relevancy and timely delivery of information for various DM personnel. This will result in enhanced interoperability and accessibility to information as well as ensure that the information does not end up as mere information overload. While many information gateways and interface roles may involve a human actor (e.g., information manager, orchestrator) [8], some capabilities may be implemented in automated elements (e.g., DSS, expert systems) positioned at major data collection points in the structure of a DM system. With regard to the importance of information gateway and interface functions, we observed visible differences in the cross-agency information flows when the level of training and authority of the information managers (i.e., human information gateway/interface) changed. The concept of well-versed “reserve corps” ([20], p. 64) who carry on their regular (small-scale) emergency responsibilities in normal situations but then are reassigned during a multi-agency DM operation to roles such as: information analyst, gateway, interface, etc. suggests a good solution to alleviating problems regarding training and authority. 5.1. Limitations and future research directions As mentioned in the research design section this study includes several limitations. Some of the limitations were inevitable because the studied subjects were real DM personnel on duty, which limited the time we had to survey and interview them after the exercise. Indeed, the restrictions on our data collection were imposed not only for privacy and sensitivity, but also to prevent any vacuum in the emergency response capacity in the surrounding regions. Nevertheless, we managed to collect 46 usable survey responses, which are extremely valuable especially considering the lack of such data in the DM domain. The limited access to emergency responders also caused a weakness in our measurement instrument and analytical technique. In order to fit the questionnaire in the given data collection time frame, we had to use a single-item measure for every construct in the model. Consequently, we could not use SEM techniques for data analysis, which leaves questions about the reliability and validity of the measurement model. While some of the practical restrictions and limitations could not be overcome, we tried to supplement the survey findings by using qualitative data. Thanks to extensive collaboration with members of the VIKING consortium we were allowed to observe all of the exercises, interview information architects and participants, and were given unrestricted access to project documents. The researchers observed all three exercises, interviewed various DM personnels involved in the exercises, and examined the documentation of the exercises and Program Viking itself. This has led us to the conclusion that the path analysis results are in accordance with the rich qualitative data and field experience. The current study conceptualizes DMIS as a package of multiple ICTs, which includes voice communication systems, message exchange systems, geographic information systems (GIS), and flood simulation systems. The situation of having many individual systems is partly due to the lack of resources and authority to implement an integrated package that satisfies all DM needs for all types of DM agencies. While the issue of integration is outside of the scope of this paper, we observed that none of the available ICTs was universally used by all participants in the exercises. Moreover, no agency had access to every ICT, which means that no agency had access to all of the information available. Therefore, the aggregation of multiple systems was necessary and appropriate in this study. However, such aggregation may cancel out some individual technology specific effects. Future research may find additional, possibly more specific effects, by examining an individual or fully integrated IS. In doing so, different or even incompatible functionalities and information requirements for various stakeholders may be identified. Future research may extend this study in several directions. First, defining IS success in multi-agency DM is a challenging, yet critical, prerequisite to evaluate and improve IS in this area. This study suggested intention to use as a reflective measure of IS success, but it is just one preferred measure rather than the best measure for all situations. The large number of stakeholders usually involved in multi-agency DM environments makes it extremely difficult to reach a consensus on the measure, as shown in our study results. In particular, the concept of expected group value may be defined at multiple levels and in multiple dimensions, each of which will have a different impact on other DMIS success measures. Therefore, we need more studies that examine various alternative success measures and relationships among those measures. This will enable practitioners to make an informed decision when they need to measure their IS success or initiate a new IS development/adoption project. A search for other major determinants of expected group values is also an important and recommended direction for future research. Although the ANOVA result suggests that there is no significant difference, in terms of the group mean values of the endogenous variables between the four types of DM agencies (see Table 3), it is still possible that the type of DM task can moderate the relationships between the variables. For example, information managers whose primary task is to support inter-agency information sharing and coordination may show a stronger relationship between perceived task support and expected group value (H2c) than some of the other DM workers. In a similar vein, an individuals' job satisfaction may differ depending on their roles in a DM operation (e.g., saving lives in the field vs. relaying messages between different agencies without understanding their meaning). This may influence their perception of the value of DMIS and thus acceptance of it. A follow-up study that validates these possibilities will provide a clearer understanding of the relationships between these variables. Replication studies with a larger dataset and measurement studies that test and refine multi-item measures are also needed for rigorous future research in this area.

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