Enterprise Architecture: A Business Value Realization Model

Alwadain, Ayed

doi:10.3390/su12208485

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Previous studies on investment decisions have mostly explored whether investment size is insufficient or excessive in terms of financing structure, agency costs, and corporate governance structure. However, there are few studies on the measurement of the investment size of enterprises, yet in practice, operators are really concerned about how large the future investment size of enterprises should be limited to be appropriate for them [18]. Therefore, in this paper, through the previous scholars' research on investment influencing factors, we try to construct a reasonable investment size prediction model that can help enterprises' investment decisions in practice [19].…”

Section: Traditional Enterprise Investment Capacity Forecasting Modelmentioning

confidence: 99%

Analysis of digital cloud accounting incorporating data mining technology in corporate investment decision making

You

2023

Applied Mathematics and Nonlinear Sciences

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This paper analyzes enterprise investment capability based on quantitative analysis under data mining technology and selects cash flow as the key indicator of enterprise investment to make a predictive analysis of enterprise cash flow. The system of enterprise investment capacity indicators is constructed, and the relationship between cash flow and each quantitative is analyzed by the regression model. The case analysis of the enterprise financing structure and the risk of bank financial indicators were conducted separately, and the results showed that the financing structure of enterprise A was dominated by long-term borrowing, which accounted for more than 60% every year, followed by bonds payable, which accounted for between 10% and 30% every year. The return on assets of Bank A is between 15% and 20%, much higher than the other two banks.

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Section: Traditional Enterprise Investment Capacity Forecasting Modelmentioning

confidence: 99%

Analysis of digital cloud accounting incorporating data mining technology in corporate investment decision making

You

2023

Applied Mathematics and Nonlinear Sciences

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“…In carrying out business transformation in organizations, Enterprise Architecture or Enterprise Architecture (EA) is the key to digital transformation that contributes significantly to the alignment of IT and business [1] [2]. EA plays an effective role in digital transformation [3], enabling its implementation to be more on time and within budget [4].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Enterprise Architecture with Leadership Moderation Effects as a Performance Model for Regency/City Local Government Agencies in West Java Province

Hanafi,

Munir,

Suwatno

et al. 2023

J. of Law and Sust. Develop.

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Purpose: The role of information technology in government agencies is becoming increasingly vital. Information Technology (IT) is positioned as an enabler, driver, and even transformer that plays a significant role in creating an organization's competitive advantage. In Government Agencies in the Republic of Indonesia, the application of SPBE (Electronic Based Government System) further legitimizes the vital role of information technology in the operations of Government Agencies in achieving organizational goals and objectives. One of the main activities in SPBE is the implementation of enterprise architecture (EA) in government agencies called the SPBE architecture. The EA implementation based on various studies conducted can positively contribute to encouraging the role of IT in achieving organizational performance. Research implications: Based on the implementation of the SPBE evaluation since 2018, there have been several improvements in the implementation of the SPBE in Government Agencies, both in terms of policy, governance, management, and IT services. The SPBE maturity index for government agencies generally increases from year to year. Government Agencies have generally started EA implementation or SPBE. However, no research can show whether it is true that EA Implementation in Local Government Agencies in the Republic of Indonesia can contribute to achieving organizational performance that represents increasing achievement of organizational goals and objectives. Method: This research was conducted at all district/city government agencies within the province of West Java. This study tries to test the hypothesis of how the implementation of EA in district/city local government agencies in West Java Province represented by EA capabilities and EA governance can contribute to improving organizational performance through organizational agility. In the relationship between EA capability and governance and organizational agility, the hypothesis testing of the leadership moderation variable is tested, whether it is true that leadership can contribute to achieving organizational agility in implementing EA. Results and conclusion: Data processing uses quantitative analysis with descriptive statistics and verification analysis using SEM. From the research, EA capability and EA governance contribute to organizational performance improvement through organizational agility. Leadership provides a positive moderating effect on the relationship between EA capability and EA governance on organizational agility.

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“…EA aims to align technology with business goals to improve decision making, reduce IT costs, optimize investment decisions, streamline business processes, and promote resource reuse across all domains (Alwadain, 2020;Bonnet, 2009;Canada & Halawi, 2021;Dang & Pekkola, 2017;Dumitriu & Popescu, 2020;Effendi et al, 2021;Niemi & Pekkola, 2020;Šaša & Krisper, 2011;Van den Berg et al, 2019). However, to verify the extent to which EA benefits are realized, EA-adapted measurement solutions are needed to measure EA performance and progress towards strategic goals (Brückmann et al, 2009;Cameron & McMillan, 2013;Wan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Scale Types and Measurement Units in Enterprise Architecture (EA) Measurement

Abdallah

Abran

Qasaimeh

et al. 2023

IJIKM

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Aim/Purpose: This study identifies the scale types and measurement units used in the measurement of enterprise architecture (EA) and analyzes the admissibility of the mathematical operations used. Background: The majority of measurement solutions proposed in the EA literature are based on researchers’ opinions and many with limited empirical validation and weak metrological properties. This means that the results generated by these solutions may not be reliable, trustworthy, or comparable, and may even lead to wrong investment decisions. While the literature proposes a number of EA measurement solutions, the designs of the mathematical operations used to measure EA have not yet been independently analyzed. It is imperative that the EA community works towards developing robust, reliable, and widely accepted measurement solutions. Only then can senior management make informed decisions about the allocation of resources for EA initiatives and ensure that their investment yields optimal results. Methodology: In previous research, we identified, through a systematic literature review, the EA measurement solutions proposed in the literature and classified them by EA entity types. In a subsequent study, we evaluated their metrology coverage from both a theoretical and empirical perspective. The metrology coverage was designed using a combination of the evaluation theory, best practices from the software measurement literature including the measurement context model, and representational theory of measurement to evaluate whether EA measurement solutions satisfy the metrology criteria. The research study reported here presents a more in-depth analysis of the mathematical operations within the proposed EA measurement solutions, and for each EA entity type, each mathematical operation used to measure EA was examined in terms of the scale types and measurement units of the inputs, their transformations through mathematical operations, the impact in terms of scale types, and measurement units of the proposed outputs. Contribution: This study adds to the body of knowledge on EA measurement by offering a metrology-based approach to analyze and design better EA measurement solutions that satisfy the validity of scale type transformations in mathematical operations and the use of explicit measurement units to allow measurement consistency for their usage in decision-making models. Findings: The findings from this study reveal that some important metrology and quantification issues have been overlooked in the design of EA measurement solutions proposed in the literature: a number of proposed EA mathematical operations produce numbers with unknown units and scale types, often the result of an aggregation of undetermined assumptions rather than explicit quantitative knowledge. The significance of such aggregation is uncertain, leading to numbers that have suffered information loss and lack clear meaning. It is also unclear if it is appropriate to add or multiply these numbers together. Such EA numbers are deemed to have low metrological quality and could potentially lead to incorrect decisions with serious and costly consequences. Recommendations for Practitioners: The results of the study provide valuable insights for professionals in the field of EA. Identifying the metrology limitations and weaknesses of existing EA measurement solutions may indicate, for instance, that practitioners should wait before using them until their design has been strengthened. In addition, practitioners can make informed choices and select solutions with a more robust metrology design. This, in turn, will benefit enterprise architects, software engineers, and other EA professionals in decision making, by enabling them to take into consideration factors more adequately such as cost, quality, risk, and value when assessing EA features. The study’s findings thus contribute to the development of more reliable and effective EA measurement solutions. Recommendation for Researchers: Researchers can use with greater confidence the EA measurement solutions with admissible mathematical operations and measurement units to develop new decision-making models. Other researchers can carry on research to address the weaknesses identified in this study and propose improved ones. Impact on Society: Developers, architects, and managers may be making inappropriate decisions based on seriously flawed EA measurement solutions proposed in the literature and providing undue confidence and a waste of resources when based on bad measurement design. Better quantitative tools will ultimately lead to better decision making in the EA domain, as in domains with a long history of rigor in the design of the measurement tools. Such advancements will benefit enterprise architects, software engineers, and other practitioners, by providing them with more meaningful measurements for informed decision making. Future Research: While the analysis described in this study has been explicitly applied to evaluating EA measurement solutions, researchers and practitioners in other domains can also examine measurement solutions proposed in their respective domains and design new ones.

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Enterprise Architecture: A Business Value Realization Model

Cited by 5 publications

References 26 publications

Analysis of digital cloud accounting incorporating data mining technology in corporate investment decision making

Analysis of digital cloud accounting incorporating data mining technology in corporate investment decision making

Implementation of Enterprise Architecture with Leadership Moderation Effects as a Performance Model for Regency/City Local Government Agencies in West Java Province

Analysis of the Scale Types and Measurement Units in Enterprise Architecture (EA) Measurement

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