Journal of Management and Engineering Integration, v.16 no.1

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    Journal of Management & Engineering Integration, v.16, no.1 (Summer)
    (Association for Industry, Engineering and Management Systems (AIEMS), 2023-06) Association for Industry, Engineering and Management Systems (AIEMS)
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    The US DOD budget: Can it be predicted?
    (Association for Industry, Engineering and Management Systems (AIEMS), 2023-06) Arbogast, Gordon W.; Jadav, Arpita
    The Department of Defense (DOD) is part of the United States Federal Government which oversees the U.S. Military. This Department is one of the largest and most complex organizations in the world. The DOD mission is to protect and defend the United States (US) and provide national security. To achieve this, the DOD requires a major portion of the federal budget. Each year, the DOD portion is based on a variety of political and economic factors. The results of this study are noteworthy. A regression model was derived that explained 82.14% of the variation in the target ratio of the federal budget with a significance level of 0.05. Four variables were identified and listed in order of greatest impact, as determined, by their standardized coefficients. These variables may have a significant relationship with DOD's budget. The four variables are: (a) House Majority Political Party, (b) Doomsday Clock Value, (c) US President's Political Party Affiliation, and (d) US Gross Domestic Product Growth Rate. If corporations and other agencies that deal with the DOD were to be able to accurately predict year-by-year DOD budget levels, this would give them a unique, competitive advantage. The strong presence of political factors in the results may be a key indicator for DOD businesses to consider in ensuring the balance of an appropriate level of politically motivated drivers within their corporate strategy models. Further recommendations focused primarily on the factors and, ultimately, the variables that should be selected for future studies. Variables need to be selected to allow for a greater number of observations to increase the likelihood of producing accurate study results.
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    Large scale analytics for workload segmentation
    (Association for Industry, Engineering and Management Systems (AIEMS), 2023-06) Hu, Bing; Mason, Nicholas
    As the complexity and size of workload landscapes continue to evolve, traditional tools that are useful for characterizing workloads have gradually become inadequate. Approaches such as scaling studies or isolated analyses of a single workload are gradually becoming insufficient to understand the broad state of the workload universe. To address this problem, we follow a machine learning methodology to leverage large numbers of workload experiments that already exist on Intel. We advanced a workload analysis platform that stores, manages, and facilitates the analysis of workload information using two big data analysis software tools, a top-down workload decision classifier and a workload universe mapping tool, to characterize large-scale workloads. Both analysis tools present a novel way to look at workload data from a higher level and consider these new characteristics at the workload segmentation level. We seek to achieve this goal in alignment with the strategic goals of developing new benchmarks, informing market sizing, and detecting emerging workloads.
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    Unhelpful and unaware of it: A dyadic analysis of online product reviews
    (Association for Industry, Engineering and Management Systems (AIEMS), 2023-06) Swain, Scott D.
    Much research focuses on identifying characteristics that predict whether consumer reviews are perceived as helpful. In contrast, little is known about whether review writers themselves know if their reviews will be helpful or whether they can be provided with effective writing prompts to improve the helpfulness of their reviews. Across two studies, the evidence suggests that while review writers are overconfident, their reviews are most helpful when their attentional focus during writing is on others (versus themselves) and when reviewing products characterized predominately by search (vs. experience) qualities.
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    Service quality between tourism and pilgrimage: A literature review
    (Association for Industry, Engineering and Management Systems (AIEMS), 2023-06) Alshaibi, Majid; Bahaitham, Haitham; Elshennawy, Ahmad
    Service quality is a key success factor in rapidly developing markets that crucially acquire customer satisfaction and retention. In this study, a systematic literature review following the PRISMA review protocol addresses service quality approaches and models. Following the service quality model's conceptualization and dimensionality, the models' applications in the tourism industry, in general, have been covered while shedding light on their applications in the Hajj event, one of the world's largest annual massive gatherings in Saudi Arabia. The outcomes of this effort are aimed at developing a novel framework with standardized, relatively comprehensive dimensions that suit Hajj service clusters and assimilated stakeholders.