Formulating optimal business process change decisions using a computational hierarchical change management structure framework

Abstract

Purpose-This paper firstly examines how business process change decisions (BPCDs) were implemented in a government organisation bound by tightly-coupled temporal constraints (TTCs). Secondly, it focuses on how to achieve optimal and efficient BPCDs that require tight compliance with regulators' temporal constraints. Finally, it formulates a rigorous framework that can facilitate the execution of optimal BPCDs with maximum efficiency and minimal effort, time and cost. Design/methodology/approach-Decision making biases by individuals or groups in organisations can impede optimal BPC implementation; to demonstrate this a case study is investigated and the formulated framework is applied to tackle these failings. Research findings-The case study analysis shows 76% of the BPCDs implemented were inefficient, mostly due to poor decisions and these resulted in negative ripple effects. In response, the newly developed hierarchical change management structure (HCMS) framework was employed to empower organisations to execute high velocity BPCDs, enabling them to handle any temporal constraints imposed by regulators or other exogenous factors. The HCMS framework was found to be highly effective, scoring an average improvement of more than 100% when measured using decision quality dimensions. This paper would be of value for business executives and strategic decision-makers engaging with BPC. Research limitations/implications-The HCMS framework has been applied in a single case study as a proof of concept. Future research could extend its application to broader domains that have multi-attribute structures and environments. The evaluation processes of the proposed framework are based on subjective metrics. Causal links from the framework to business process metrics will provide a more complete performance picture. Practical implications-The outcome of this research assists in formulating a systematic BPCD framework that is otherwise unavailable. The practical use of the proposed framework would potentially impact on quality outcomes for organisations. The model is derived from decision trees and analytical hierarchical processes and is tailored to address this problematic area. The proposed HCMS framework would help organisations to execute efficient BPCDs with minimal time, effort and cost. The HCMS framework contributes to the academic literature on BPCD that leverages diverse stakeholders to engage in BPC initiatives. Originality/value-The research presents a novel framework − the hierarchical change management structure − that provides a platform for organisations to easily determine and solve hierarchical decision structure problems, thereby allowing them to efficiently automate and institutionalise optimal BPCDs.

Proactive Management of Regulatory Policy Ripple Effects via a Computational Hierarchical Change Management Structure

“…Therefore, designing a framework to elicit, compute and identify causes would be an appropriate solution. The computational hierarchical change management structure (HCMS) framework developed by (Alrabiah and Drew 2018b) quantifies and analyses the time, cost and impacts in each node (see Figure A4 in Appendix D). Further, the causal analysis framework technique strengthens the competence to develop a regulatory policy framework (Fischer and Miller 2017;Loi and Rodrigues 2012).…”

“…Further, this paper extends the work by (Alrabiah 2018) that proposes a standardisation and integration framework to ideally and proactively regulate the banking system. Also, the paper uses the HCMS framework proposed by (Alrabiah and Drew 2018b). Figure A4 shows an example of the adopted HCMS.…”

“…Currently, many CBs and regulators face different challenges when implementing or changing policies (Anagnostopoulos and Kabeega 2018;Capano and Woo 2017;Constâncio et al 2019;Freixas et al 2015). Banks, financial firms and regulated-entities have different polymorphism environmental setups, such as in hierarchical structures, technology infrastructures, business strategies, business operations and firm sizes (Alrabiah and Drew 2018b;Brent and Addo 2012). Reports indicate that the policy implementations of most CBs were inefficient (suboptimal) because of information quality (Coeuré 2017b;IMF 2009;Tosun and Treib 2018).…”

“…The framework shown in Figure 2 was adopted and tailored from the HCMS Framework developed by (Alrabiah and Drew 2018b). We customised the HCMS framework to fit with the regulatory policy implementation system in our case study (SAMA).…”

“…(Fischer and Miller 2017;Loi and Rodrigues 2012). The phenomena of ripple effects in the business domain are defined as the inconsistency or the unexpected outputs of one or more actions that produce impacts on other schemes or bodies (Alrabiah and Drew 2018b;Lee 1998;Luftman et al 1993;Yau et al 1978). Thus, these behaviours probably generate impacts in different directions and lead to the propagation of those effects (Khan 2018).…”