Hu et al., 2020

Abstract. This study introduces a dynamic decision architecture that involves three steps for corporate performance fore-casting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performanceevaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decisionmaking trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the compli-cated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specificdirection, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracingone-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective.By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKORis subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into randomvector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instancelearning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases,offers a promising alternative for performance evaluaAbstract. This study introduces a dynamic decision architecture that involves three steps for corporate performance fore-casting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performanceevaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decisionmaking trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the compli-cated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specificdirection, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracingone-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective.By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKORis subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into randomvector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instancelearning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases,offers a promising alternative for performance evaluation and forecasting. Abstract. This study introduces a dynamic decision architecture that involves three steps for corporate performance forecasting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performance evaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decision making trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the complicated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specific direction, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracing one-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective. By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKOR is subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into random vector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instance learning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases, offers a promising alternative for performance evaluation and forecasting.


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Jacqueline Shackil

Jacqueline has an M.S. in Applied Behavior Analysis and is working towards a Graduate Certification in Industrial-Organizational Psychology. She is also a Board Certified Behavior Analysis.

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