User Satisfaction Assessment of a Developed Temperature Monitoring System Based on A-Priori Information System Impact Model

User Satisfaction Assessment of a Developed Temperature Monitoring System Based on A-Priori Information System Impact Model

Arneda Jean Gonzales, Celbert M. Himang, Melanie M. Himang, Rebecca Manalastas, Miriam F. Bongo, Lanndon Ocampo
Copyright: © 2021 |Pages: 19
DOI: 10.4018/IJSKD.2021040105
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Network downtimes are typically addressed by deploying a temperature monitoring system (TMS) designed to detect variations in temperature and signal users of an impending increase of temperature. The development of a TMS has been found to essentially monitor temperature states across various case applications, but a concurrent analysis of such system was not carried out despite the need to recognize user satisfaction for improvement of future TMS information system components. Thus, this paper seeks to design, implement, and analyze a TMS under the a-priori information system (IS) impact model, which incorporates the constructs on individual impact, organizational impact, information quality, and system quality, in an academic institution as case environment. It is found that all four constructs have a positive influence on the proposed TMS IS impact except for organizational impact. Under such a case, decision-makers are prompted to develop a product that anchors primarily on individual impact, information quality, and system quality to attain desired system outputs.
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1. Introduction

In the advent of interconnected networks performing to provide a competitive advantage to the organization’s operations, significant benefits have emerged from the development and implementation of sensor technology as a support to such networks (Neethirajan, 2017). Organizations tend to integrate intelligent networks to facilitate and enhance a more convenient manner of carrying out operational tasks (La et al., 2019). With such goals in mind, operational benefits are expected to be achieved in various forms such as very high aggregate data rates for telecommunication networks in specific (Abeywickrama et al., 2016), data transfer from one point to another (Becker et al., 2015), and provide a competitive advantage to market actors over others (Burt, 1992; Czakon & Kawa, 2017). In fact, networks have been successfully applied in several domains including healthcare (Egan & Liu, 1995), transportation (Shi & Abdel-Aty, 2015), cyber-physical systems (Noor et al., 2019), offshore platform (Uçaktürk, A., & Villard, 2013), and nuclear power plants (Kim et al., 2014), among others. Despite the potential advantages that the implementation of networks offers, issues associated with its functions remain to be evident. They pose as threats on its continuous functionality as in network downtime (Chimoriya, 2016). Furthermore, when networks fail, several aspects of firms’ operations are hampered accordingly with eventual evidence in the reduced efficiency of employees’ tasks, including the services provided by firms in general. That is, underlying network elements significantly affecting the overall performance of network services leading to detrimental, if not catastrophic, results (Ahmed et al., 2016). Network downtime emerges due to various factors, or a combination thereof, such as hardware failure, carrier network outage, human error, and configuration problems (Acronis, 2014). Typically, network downtimes are commonly caused by overheating of devices (Dalsgaard et al., 2006) which mainly results to lost interchange revenues and failed customer interactions (Putting intelligence back into the ATM network, 2008), variable downtime costs and age-dependent running costs (Smith et al., 1997), delays in operations (Wang et al., 2016), and cost of unavailability (Anscombe, 2011), among others.

To address the overheating of devices that cause network downtime, proprietary network solutions are marketed and designed to cater mainly to a complicated scale of networks. Moreover, organizations are given the liberty to customize these solutions according to their organizational needs at a relatively higher cost (GCS Technologies, 2016). Recognizing that overheating of devices which leads to malfunction of networks can be remotely monitored, temperature monitoring devices are essentially installed to prevent the actual damage of devices (Dalsgaard et al., 2006; Mind Commerce Staff, 2015). A concrete illustration of such solutions can be found in embedded systems that employ real-time alerts to minimize, if not to avoid, network downtime (Rudeck, 2016). These embedded systems automate and enhance network management systems and, at the same time, collect and analyze data transmitted from remotely installed devices.

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