Cognizant computing + transformative marketing: an intelligent solution for sustainable business development

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Cognizant computing +
transformative marketing:
an intelligent solution for
sustainable business development
1. Introduction and background
Cognizant computing (CC), a revolutionary computing paradigm, which is still in its nascent
stages of development, has heralded tremendous utilities and benefits for several areas of
business endeavours. From production and manufacturing through lean practices to
advertising and sales by way of consumer behaviour analytics, recent research lends
credence to earlier assertions of the transformations that CC is able to bring to modern business
practices. Indeed, much of the foundational technological capabilities that are needed to scale
the impact and utilities of CC are already in place, such as the ability for devices to act “smart”,
i.e. receive instructions and take action by setting alarms and reminders, make payments for
goods and services, contact particular individuals, open applications and carry out operations,
posting and sending information, etc. (Ronkowitz, 2013); all these technologies are based on
data that can be collected locally and then provisioned, accessed and processed ubiquitously.
Awodele et al. (2014) define CC as a consumer-centred computing paradigm that applies
the personalization of services on an individualistic level to meet the specific needs of
consumers by enhancing their experience. This definition is in tandem with that given by
Volico (2017), and they both present the idea that digital systems, through simple rule-based
computational algorithms (The Economic Times, 2014), are able to take decisions that mirror
the best choices or preferences of consumers under certain transactional circumstances/
situations, typically by analysing their digital footprint (data) to deduce patterns in their
decisions from antecedent similar situations.
One of the early research studies that featured the idea of CC was a market research study
by Gartner (Ekholm, 2013), although, in a general sense, the concept of “cognizant”as a
technological term was not particularly new/unheard at the time. This term generally
embodies the concept of a system or application being “aware”of a particular operational or
behavioural reality and ultimately applies that awareness in taking/enforcing decisions and
constraints that are in the best interest of particular subjects.
In the context of CC, this “awareness”is consumer-focused. The utility that CC presents is
fundamentally a function of the depth and quality of the digital data that are available for
processing about consumers; the available data may be collected, processed and stored
locally on the users’device(s) or delocalized in the cloud. Then, these data are fed into larger
platforms and operating systems/processes to create an ecosystem of functions, operations
and realities that are relevant to particular consumer experiences across a wide range of
contexts, devices and utilities (Bremmen, 2013).
This is made possible by two foundational emerging technologies (Awodele et al., 2014):
the Internet of Things (IoT), which pervasively accumulates client/user data from across
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Author contributions: VOB contributed all content on transformative marketing and designed the
structure of the paper. ECO contributed all content on cognizant computing and compiled the paper
based on an outline agreed to by both authors.
Business Process Management
Journal
Vol. 27 No. 6, 2021
pp. 1926-1934
© Emerald Publishing Limited
1463-7154
DOI 10.1108/BPMJ-10-2021-595

various interconnected sources, interfaces, devices, systems and applications (Sisinni et al.,
2018), and cloud computing, which consolidates and provisions this (big) data, so that it can be
accessed, processed, manipulated and analysed ubiquitously to avail business intelligence
on-demand (ElMalah and Nasr, 2019). Both these technologies provide the backbones for
aggregating customer/client data in this manner to be applied for various data-driven utilities
towards improving business processes, enhancing administrative tasks and powering
development. These technologies feature capabilities for automated decision-making,
innovation, market research, customer insights and management and enterprise resource
planning, to mention a few.
Today, as was predicted in the early days (Nigeria Communications Week, 2014), this
emerging technology has revolutionized the mobile computing industry, through a
transformative strategy that enables the design of mobile applications that are more
consumer-/user-aware (Bolkan, 2014) across their entire spectrum and routine of operations.
Beyond this, existing research has further conceptualized the utilities that this paradigm is
able to bring to particular fields of human endeavour, such as in manufacturing, for
informing lean thinking/practices to cut down wastage in production operations, while at the
same time scaling system-wide Industry 4.0 digital transformations (Ogu et al., 2018).
Similarly, transformative marketing (TM) is another innovative paradigm that has helped
businesses respond effectively to trends and changes in market environments by unifying
concepts, strategies, processes, metrics, programs and activities across organization-wide
(marketing) apparatuses to deliver value to customers that exceed competing offerings and bring
profit to all business stakeholders. It is in this light that (Kumar, 2018,p.2)definesTMas“the
confluence of a firm’s marketing activities, concepts, metrics, strategies, and programs that are in
response to marketplace changes and future trends to leapfrog customers with superior value
offerings over competition in exchange for profits for the firm and benefits to all stakeholders.”In
essence, the goal of TM is to create a superior strategy that unites business functions across
departments, processes and operations in response to marketplace trends (Kumar, 2018).
Essentially, this is aimed at helping businesses reach their intended audiences with
superior competitive offerings aimed at satisfying customers (Farooq and Raju, 2019a) with
better precision, while avoiding the risks and ethical challenges of overconsumption
(Carrington et al., 2012) and overproduction/wastage (Termeer et al., 2019). While, at the same
time, applying procedures that consolidate the best interests of all stakeholders within a
social process of co-responsibility (Manoharan et al., 2021) and sustainability (Varey, 2010).
Several cutting-edge technologies and tools have helped to scale the utility that TM has
brought to companies (Mikalef et al., 2018), with an impact that has been felt across multiple
business domains and sub-domains, like services operation, supplier management, process
optimization, customer experience, production efficiency, etc (Kumar et al., 2021). These
technologies include cloud computing, the IoT, machine learning, artificial intelligence,
blockchain, among others. Recent research has equally explored contemporary issues of TM
in the digital era, even across various business and organization contexts spanning the
telecommunications (Farooq and Raju, 2019b), education (Tijjani, 2019;Dixon-Todd et al.,
2017) and e-commerce (Farooq et al., 2020;Farooq and Qureshi, 2020) sectors.
The big question thus arises: Where do CC andTM meet? In coordination, what prospects do
they present for businesses? How can businesses be better positioned to harness these twin
paradigms for enhanced sustainability? Until now, the existing research landscape has studied
these two paradigms in isolation, fundamentally segregating the utilities that they present
independently for particular business models, processes and operations, from customer
management in e-commerce to lean practices in manufacturing and production, and consumer
analytics for effective advertising and improving sales, to mention a few. But then, this paper
consolidates existing knowledge to interrogate the nexus between these revolutionary
paradigms, in light of the converging utilities that they present for amplifying productivity,
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minimizing risk,increasing returns on investment (RoI),optimizingbusiness processes,enhancing
competitive advantage and scaling market impact and business value proposition,even
sustainably for businesses and organizations in the age of digitalization.
2. Cognizant computing +transformative marketing: an intelligent solution for
effective business administration
Janiesch et al. (2017) discuss critical points that are to be considered for a smooth integration
and deployment of digital information and computing technologies for Business Process
Modelling (BPM). Here, these critical points (combining some of them) are adapted by
interposing and infusing the concepts of TM and CC to reflect a necessary balance between
business process modelling and customer-facing technological innovation/digital
transformation. Then, this adaptation is applied towards elucidating the nexus between
CC and TM as a unified intelligent solution for sustainable business administration and
development in the 21st Century. These critical points include:
(1) Ensuring that data aggregated from different sources reflect the realities of underlying
processes: The integrated frameworks and technologies that power CC allow data to be
collected and aggregated in such a form that is not only relevant for ongoing business
processes but also reflective of the underlying realities from which such data were
generated (Sisinni et al.,2018). To the extent that TM is able to model these realities
correctly, CC is able to accumulate data based on these modelsto drive business utility.
(2) Synchronizing manually executed, physical processes: Within the context of
technology-enabled firms and business processes, business process management
systems integrate with digital solutions that are often powered by mobile
applications to automate physical processes that would otherwise have needed to
be executed manually by business stakeholders (employees, vendors and customers)
in operational circumstances. CC allows data to be synchronized across these
processes with insights (ElMalah and Nasr, 2019) that relevantly support the goals of
management in adding and preserving value for all participating stakeholders.
(3) Integrating analytical processes: Within the paradigm of CC, cloud computing
provides the backbone environment within which analytical processes and
operations could be executed on business-related data that are accumulated from
diverse sources. These data are not only relevant in terms of history and origin but
are also provisioned in a current and up-to-date manner that is useful for generating
insights in real time to inform the tasks and responsibilities of business
administration, management and decision-making.
(4) Integrating process correctness checks: The emerging paradigm of blockchain of
things (BCoT) (Dai et al., 2019) portends great prospects for process efficiency and
correctness in digital technology-driven business operations. By enabling a
collaborative approach to error-checking, security verification and managing
concurrency, BCoT brings in efficiency that amplifies the potentials of IoT for
business administration, while diminishing existing concerns and apprehensiveness
towards the utilities of CC as an intelligent business solution.
(5) Dealing with unstructured environments: The pursuit of TM helps firms to better
interpret and understand peculiarities within their business environment(s). In the
age of digitalization, the integration of digital marketing concepts and utilities in
modelling the firms’interpretation and understanding of these peculiarities would
help firms to give structure to the stochastic realities of the business domain.
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However, this is not without inherent risks posed by device and system faults and
failures. BCoT provides tolerance to such faults and failures (whenever they arise);
while also ensuring that the on-boarding of new processes, systems and devices does
not create new errors or perpetuate existing one. This is pursued through resilient
collaboration (Dempsey and Kelliher, 2018) that has minimal impact on the dependent
business processes and operations that are currently taking place.
(6) Managing the links between micro-processes: Cloud computing allows data-driven
digital processes to be inter-linked in ways that can be easily understood and
interpreted. By organizing processes and the data that pertains to them into objects
through the principles of self-organizing mapping, it becomes possible to (perpetually
or dynamically) link micro-processes into hierarchical multi-dimensional patterns
and structures that can be managed, manipulated and analysed in a top-down or
bottom-up coordinated manner.
(7) Breaking down end-to-end processes: In the pursuit of transformation, Melkonyan
et al., (2019) advocate for an approach that features an integrated assessment of
production and consumption systems, also taking into consideration the interests of
key stakeholders at the macro-economic level.
Such an integrated approach allows event-driven micro-processes to model the
intricacies and behaviours of the underlying data frameworks that drive these
individual processes, even in such a way that aligns with the interests of stakeholders at
the higher level. Effective TM provides a means for structuring these micro-processes
across business functions with minimal complexity, while CC would provide a way to
manage and organize these processes in a way that reflects an awareness of the
semantics and relationships in the underlying data frameworks.
(8) Detecting new processes from data: Essentially, the role of analytics is to represent,
forecast or predict future/alternative outcomes based on insights from data that
provide knowledge about current situations and realities. However,within the context
of CC, the results of such analytics are further applied to initiate new consumer-facing,
profit-yielding transactions, processes and actions that meet the needs of business
stakeholders with minimal errors based on prospective knowledge that is informed by
data and driven by computational rules that are goal-based and interest-oriented.
(9) Dealing with the autonomous capabilities of digital information technologies in
business applications: The autonomous capabilities of the digital information
systems and technologies that pervade modern business context continue to pose
concerns for stakeholders (Subramanian and Jeyaraj, 2018;Arora et al., 2019).
However, through a collaborative approach to building consensus at critical points
when autonomous action is desired, it becomes possible to avert outcomes that could
bring about unpalatable consequences in the long run by co-creating value (Parsons
et al., 2021) through transformative innovation (Parkinson et al., 2019). Indeed, the
utility that BCoT provides in this context, as an emerging integration for CC, can be
extended to check the autonomous capabilities of digital information technologies in
this way.
(10) Specifying and aligning roles with goals: When TM succeeds in helping firms to better
interpret and understand peculiarities within their business environment(s), it becomes
possible to model these realities to reflect the various participant/stakeholder roles, as
well as the organizational goals that the functions of each role contributes to. In a multi-
factorial business context, CC provides a means of synchronizing and reconciling these
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roles from across various data-driven sources and business functions and then flexibly
aligning these roles with specific organizational goals. Sothat each role is treated with a
uniqueness that is reflective of its peculiarities, and conflicts with other roles and
functions are minimized.
(11) Concretizing abstract process models: TM helps businesses to better understand
certain realities of their domain of operation, thus making it possible to design such
realities as abstract process models that are not concerned with the specific details of
implementation and execution. But then, in concretizing such models, it is important
that contextual intricacies (data, devices, location, transactions, etc.) are not excluded,
if such models are expected to hold any practical relevance for the domain of
application. Digital information technologies make it possible to capture these
contextual intricacies, so that abstract process models remain valid and can be
concretely applied to practical business situations.
In the context of CC, these digital information technologies avail even much more.
They are able to not only preserve the context of abstract process models but also
integrate analytics and forecasting utilities to deduce when the context being
preserved begins to vary from the context in reality.
(12) Dealing with new situations: Dealing with new situations in a business context can be
complex. Often requiring that decision-making is structured and reflects foresight, so
that quality is improved while saving time and cost (Sorescu, 2017;Chen et al., 2017).
CC makes it possible for best-choice processes and tasks to be recommended,
triggered through automation and event-based computational rules and monitored
while in execution (so that auditing is made possible through logs). This is based on
robust analytics that is data-driven and adequately captures the intricate functions
and realities of the operating context (Zhang and Yue, 2020).
(13) Bridging the gap between event-based and process-based systems and optimizing
resource utilization: In process-based business systems, the need to bridge the gap
between data that is aggregated from multiple points and the mining of process
events from log files presents a non-trivial problem, especially because such data
could emerge from interleaved operations and activities, which must be tracked at
micro- and macro-levels and processed/analysed to generate relevant high-level
knowledge.
The cloud computing feature of CC not only brings together multi-point data from
across diverse business functions and operations but can also integrate robust
mining and analytics functions to discover events of interest in these data, with
remarkable accuracy. Hence, underlying interactions can be identified as processes,
and time and resources can be conserved through optimization.
Further, with effective organizational governance structures, the analytics
insights that are availed by CC with its associated technologies can increase the
efficiency of decision-making through administrative and management processes
that are proactive and reflect foresight (Ogu et al., 2014) rather than being reactive and
limited. This way, the resources that would otherwise have been expended or wasted
in retracting management missteps, rolling back uninformed administrative
decisions or abruptly terminating organizational processes that have already gone
into execution, are preserved.
(14) Improving conformance checking, resource monitoring and quality of task
execution: It is important that beyond formulating and designing robust process
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models for various transformative business functions and operations, actual
organizational systems in execution conform to the specifications of this model. In
traditional business settings, such conformance verification tasks can be lengthy
and arduous even when the imperative of monitoring resource consumptions of
processes in execution and the quality of actual execution operations are added to
such mandates.
Digital information systems and technologies, like CC, provide effective approaches to keep
pace with such tasks, even in real time. So that incidents could be forestalled to avert
consequences that might prove inimical to business interests and goals.
Thus, it is easier to understand how CC and TM can unite to help firms in intelligently and
effectively tackling some of the most crucial challenges to sustainable business
administration in the modern business environment. By providing a repertoire of solutions
and utilities, firms could tap into to amplify productivity, minimize risk, increase RoI,
optimize business processes, enhance competitive advantage (Ajah and Nweke, 2019),
optimize resource allocation and consumption, and scale market impact through business
value proposition.
However, without shrewd entrepreneurship, businesses could miss out on these
promises that CC and TM present. Whereas individual entrepreneurs are crucial sub-
systems within the total entrepreneurial eco-system (Maas and Jones, 2015), the broader
society and industry are equally important elements of the equation that determines a well-
balanced entrepreneurship support system (Lenihan, 2011), maintains that entrepreneurial
support be tailored to the needs of smaller firms or SMEs, allowing them to develop
capabilities to operate successfully within a knowledge-driven environment characterized
by accelerated innovation. In this regard, a dynamic approach that necessitates the
building of innovation networks is needed. It is herein that TM and CC provide the
entrepreneurial leverage for developing new skills and engaging in active collaboration
among all components of the entrepreneurship eco-system where an optimal balance is
required to facilitate the economic, cultural, social and public support that businesses need
to thrive.
3. Conclusion
In conclusion, Meyer, (2018) opines that successful organizations, such as Apple,
Amazon and Walmart, do not just observe their internal and external business
environment and immediately copy their rivals’marketing processes. But they develop
a deep understanding of the factors or forces that drive change and use that as a basis
for formulating informed speculations about where the best returns on investment will
ultimately come from. Such that through routine and periodic modifications to business
processes, supported by innovative entrepreneurship, significant improvements
emerge (Watanabe and Tou, 2019). Indeed, success in such a dynamic and
unpredictable business environment is dependent upon firms continuously engaging
in data-driven research and innovations (Mikalef et al., 2018) in order to stay relevant
and profitable.
This paper has presented CC and TM as mutually-complementary paradigms that, when
supported by shrewd and innovative entrepreneurship, provide an intelligent solution to
many of the challenges to sustainable business administration and development in the
modern business environment of the 21st century (Ogu et al., 2014a, b). In contributing to the
essential literature and knowledge in this emerging scope, we hope that the discussions of
this research helps to foster progressive dialogues and engagement between business
process managers, corporate executives and administrators, and digital development
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professionals and engineers. Ultimately, leapfrogging the adoption of this twin paradigm
meets complex challenges in modern business contexts.
Valerie Onyia
Department of Business Administration and Marketing, Babcock University,
Ilishan-Remo, Nigeria, and
Emmanuel C. Ogu
Department of Information Technology, Babcock University, Ilishan-Remo, Nigeria
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About the authors
Dr Valerie Onyia is a faculty member at Babcock University, Nigeria. She holds a Master’s degree in
Human Resources Management from the University of Birmingham, UK and a PhD in Business
Administration from Babcock University, Nigeria. She teaches Principles of Management and Business
Entrepreneurship to undergraduate students and Industrial Psychology, International Marketing and
Human Resources Management to postgraduate students. Her research interests include Human
Resources Management, Mentorship, Entrepreneurial Leadership Development and Organizational
Psychology.
Dr Emmanuel C. Ogu is an aspiring Tech Diplomat, who is currently a trained Computer Scientist, a
Chartered IT Professional, a Cybersecurity Specialist, a Technology Governance and Digital
Development Expert and a Sustainability Researcher. He holds a PhD in Computer Science from
Babcock University, Nigeria. His research intersects with contemporary discourses in the areas of
Digital Development (ICT4D); Business Information Systems; Cybersecurity; Technology Policy and
Governance and Sustainability.
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