Formulating optimal business process change decisions using a computational hierarchical change management structure framework. A case study
| DOI | https://doi.org/10.1108/JSIT-08-2017-0069 |
| Date | 14 May 2018 |
| Pages | 207-240 |
| Published date | 14 May 2018 |
| Author | Abdulrahman Alrabiah,Steve Drew |
| Subject Matter | Information & knowledge management,Information systems,Information & communications technology |
Formulating optimal business
process change decisions using a
computational hierarchical change
management structure framework
A case study
Abdulrahman Alrabiah
School of Information and Communication Technology,
Griffith University Gold Coast Campus, Southport, Australia and
Saudi Arabian Monetary Authority, Riyadh, Saudi Arabia, and
Steve Drew
Tasmanian Institute of Learning and Teaching, Academic Division,
University of Tasmania, Hobart, Australia
Abstract
Purpose –This paper first aims to examine how business process change decisions (BPCDs) were
implemented in a governmentorganisation bound by tightly coupledtemporal constraints (TTCs). Second, 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 facilitatethe execution of optimal
BPCDs with maximumefficiency and minimal effort, time and cost.
Design/methodology/approach –Decision-making biases by individuals or groups in organisations
can impede optimalBPC implementation; to demonstrate this, a case study is investigatedand the formulated
frameworkis applied to tackle these failings.
Findings –The case study analysis shows 76 per cent of the BPCDs implemented were inefficient,
mostly because of poor decisions, and these resulted in negative ripple effects. In response, the newly
developed hierarchical change management structure (HCMS) framework was used 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 per cent 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 appliedin a single case study as
a proof of concept. Future research could extendits 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
performancepicture.
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
hierarchicalprocesses and is tailored to address thisproblematic area. The proposed HCMS frameworkwould
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.
Optimal
business
process change
207
Received27 August 2017
Revised14 March 2018
Accepted22 April 2018
Journalof Systems and
InformationTechnology
Vol.20 No. 2, 2018
pp. 207-240
© Emerald Publishing Limited
1328-7265
DOI 10.1108/JSIT-08-2017-0069
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1328-7265.htm
Originality/value –The research presents a novel framework –HCMS –that provides a platform for
organisations to easily determineand solve hierarchical decision structure problems,thereby allowing them
to efficientlyautomate and institutionalise optimal BPCDs.
Keywords Business process change decisions (BPCDs), Hierarchical change management structure
(HCMS), Tightly coupled temporal constraints (TTCs)
Paper type Research paper
1. Introduction
This paper examines the uses of business process change (BPC) initiatives in the public sector
that are imposed by government or regulators with tightly coupled temporal constraints (TTC)
(i.e. minimum time and cost). Bound by these temporal constraints, organisations have often
failed to execute BPC because of incomplete foundations for the decision-making structure
(Gong and Janssen, 2017;Harmon, 2014;Jurisch, 2014;Raghu and Vinze, 2007). Business
process change decisions (BPCDs) made under these conditions often result in mediocre
quality, sub-optimal BPC implementation and non-compliance with government requirements
(Grover and Otim, 2009;Jurisch, 2014). Grover and Otim (2009) argued that reaching optimum
decisions involves a systematic and holistic quantification of all BPC factors. However, current
BPC frameworks fail to achieve this. Ideally, BPCD involves the application of plausible
decision models to optimise BPC implementation (Han, 2003;Sikdar and Payyazhi, 2014).
Optimal BPC implementation is based on a well-structured formulation of decision-making
(Guha et al., 1997;Harmon, 2015;Jurisch et al., 2016;Vanhoenacker et al.,1999). However,
contemporary organisations are encountering BPC challenges in public sectors because of the
complex intra and inter-organisational relationships that trigger ripple effects, chaotic business
system behaviour (Gong and Janssen, 2017;Kherbouche et al.,2013;Luftman et al., 1993;
Snowden and Boone, 2007), decision-making biases and other anomalies (Grover and Otim,
2009;Harmon, 2010;Jurisch, 2014;Jurisch et al., 2013). In the business context, ripple effects are
explained as the collision between outcomes of activities in one system or process, prompting
impacts to one or more related processes or systems (Lee, 1998;Luftman et al., 1993;Yau et al.,
1978). There is, therefore, a demonstrated need to explore the causes of these problems, and
create a foundation for a quality, high momentum decision-making model to tackle these
impediments. Therefore, this aspires the researchers to develop an efficient and automated
BPCD framework that can analyse and solve these issues.
The research was designed to address imperfectionsin BPC decision-making that are not
adequately explored or not previously addressed in the literature (Grover and Otim, 2009;
Jurisch et al.,2012). To explore this issue further, we selected the Hafiz Program in the
Kingdom of Saudi Arabia (KSA) as a casestudy for implementing and testing the proposed
framework. The Hafiz Program is an initiative for resolving growing unemployment
problems by providing financial incentives for genuine job seekers (Al-Harbi, 2012;
Aluwaisheg, 2012). The Ministryof Labour was asked to implement the BPCD intervention
in less than three months. This set the project on an aggressive BPC path and imposed on
the ministry a set of very complex constraints to solve unemployment problems within a
short period of time which we refer to in this paperas TTCs. In addition, the Hafiz Program
had complex relationships and enormous stakeholders’diversity such as government and
private agencies that connected to validate the eligibility of job seekers, presenting the
ministry with colossalchallenges in achieving its objectives.
These challenges can be summarised as a lack of time and resources, conflicting
information and complexrelationships (Grover and Otim, 2009;Harmon, 2010;Jurisch, 2014;
Jurisch et al.,2013). Furthermore, ad hoc decisions were made by executives and senior
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management based on partial information, providing narrow alternatives and promoting a
focus on nonstrategic values, all of which exacerbated the challenges. Consequently, to
achieve the ultimateobjectives of BPCD implementation, the organisation(Hafiz) was forced
to operate in a “firefighting”mode –handling emergencies as they arose rather than
approaching the process in a rational manner with attention to medium- and long-term
planning.
To assist in the resolution of these challenges, a new computational hierarchical change
management structure (HCMS) framework is proposed by this study to formulate and
institutionalise BPCDs. The HCMS is derived from extant literature that has described
decision trees, hierarchicaldecision processes and system dynamics as BPC tools (McNamee
and Celona, 2005;Paul et al.,2014;Rosenberg et al., 2014;Sterman, 2000). The paper usedthe
novel workshop-based model, named the HCMS elicitation workshop (HEW) by Alrabiah
and Drew (2018), to facilitate and enable the constructionof the proposed HCMS framework
by systematically eliciting the information from diverse stakeholders (Cardoso et al.,2013).
The principal rationale for this frameworkwas to enable an organisation to model its BPCD
precisely and plausibly (Houy et al.,2015). It also supports the use of computer systems to
optimise decision quality and minimise the time required for the execution of rigorous
decision processes(Ploesser et al., 2008), thereby complying with any TTC.
The objectives of this research were the acquisition of an in-depth understanding of the
issues that impede optimal BPCDs and to focus on critical points such as the impacts of
complex decisions on BPC. A further objective was to contribute to the BPC theory by
proposing a systematic solution for BPC decision management problems that are specifically
generated from improper decisions. Therefore, the research paper seeks to identify the optimal
and efficient BPC implementation alternatives by applying decision science.
The proposed HCMS framework solves some criticalissues in BPCD that have not been
satisfactorily addressed, such as the effects of a complex hierarchical structure, mixed
relationships, ripple effects, chaotic behaviours and decision-making biases. As a result,
because of the organisation’s complexcausal relationships (CRs), the negative ripple effect –
especially where there are TTCs in BPC –poses a greatchallenge because it introduces new
problems, devalues the efficiency of BPC implementation, and may induce a chaotic effect
(Rosenberg et al.,2014). Thus, this research paper seeks to introduce a systematic
foundation for an optimal and efficient BPCD framework that enables an organisation to
quickly and efficiently make BPCDsfrom holistic perspectives. Many decision makers have
different preferences, values or interests which influence different business processes. In
some cases, issues are intentionally hidden, which has a significant effect on BPCD.
Specifically, behaviours associated with biases and anomalies exist in organisations and
affect the efficiency of BPCD. To restructure and reprioritise effectively, organisations
require a usable decision-making model that can systematically rate and evaluate
alternatives (Alotaibi and Liu, 2017). One such model is the decision trees (McNamee and
Celona, 2005). This model uses statistical probability to define the best alternatives for
undertaking an action. Other models include the analytical hierarchy process (AHP)model,
which uses criteria and sub-criteria structure techniques to identify alternatives (Gwo-
Hshiung et al.,2011), and the Bayesian Networks probability model, which determines a
sequence of variables through nodal connections(Seligman et al.,2000).It was decided that
the decision trees would be the most effectivein automating the design of BPCDs (Paul et al.,
2014). Decision trees offer a visualisation technique that assists to develop a BPCD
framework that will compute, measure,govern, classify, explain and evaluate (Goodwin and
Wright, 2014;Grunig and Kuhn, 2013;Quinlan,1986). These advantages give decision trees
the edge over otherapproaches.
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process change
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