Content uploaded by Aminu Mohammed
Author content
All content in this area was uploaded by Aminu Mohammed on Jul 14, 2024
Content may be subject to copyright.
Association for Information Systems Association for Information Systems
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)
AMCIS 2024 Proceedings Americas Conference on Information Systems
(AMCIS)
August 2024
IT Consumerization: A Digital Innovation Driver for Resource-IT Consumerization: A Digital Innovation Driver for Resource-
Constrained Contexts Constrained Contexts
Aminu Mohammed
Addis Ababa University
, aminu.mohammed@aau.edu.et
Solomon Negash
Kennesaw State University
, snegash@kennesaw.edu
Follow this and additional works at: https://aisel.aisnet.org/amcis2024
Recommended Citation Recommended Citation
Mohammed, Aminu and Negash, Solomon, "IT Consumerization: A Digital Innovation Driver for Resource-
Constrained Contexts" (2024).
AMCIS 2024 Proceedings
. 2.
https://aisel.aisnet.org/amcis2024/sig_dite/sig_dite/2
This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic
Library (AISeL). It has been accepted for inclusion in AMCIS 2024 Proceedings by an authorized administrator of
AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org.
IT Consumerization for Digital Innovation
Thirtieth Americas Conference on Information Systems, Salt Lake City, 2024 1
IT Consumerization: A Digital Innovation
Driver for Resource-Constrained Contexts
Emergent Research Forum (ERF) Paper
Aminu Mohammed
Addis Ababa University
aminu.mohammed@aau.edu.et
Solomon Negash
Kennesaw State University
snegash@kennesaw.edu
Abstract
This study-in-progress investigates the consumerization of IT (CoIT) in resource-constrained contexts and
its potential for fostering employee-driven digital innovation in emerging economies. Drawing on adaptive
structuration theory for individuals (ASTI) and the theory of diffusion of innovations (DOI), a
comprehensive research model on the relationship between individuals’ digital ambidexterity and digital
creativity is presented. A mixed-method approach to data collection and analysis is proposed. We contribute
to the literature by extending an existing model with the moderating role of resource constraints.
Furthermore, individuals’ empowerment with IT and the malleability of consumer information technology
are also proposed to amplify individuals’ digital ambidexterity. Practically, this research aims to inform
management in similar contexts to recognize and effectively manage CoIT as a digital innovation driver.
Keywords
IT consumerization, digital creativity, digital innovation, resource-poor economies, ASTI.
Introduction
With the rapid advancement in consumer information technology (CIT) such as smartphones, personal
digital assistants (PDA), etc., and the increasing pervasiveness and ubiquity of mobile computing systems,
the use of personal devices and consumer information systems (e.g., WhatsApp, Telegram, etc.) at the
workplace, known as the Consumerization of IT (CoIT) (Harris et al. 2012; Junglas et al. 2018), has become
a common phenomenon (Dang-Pham et al. 2019; Leclercq-Vandelannoitte and Bertin 2018). Some
institutions reject it due, in part, to security and privacy concerns (Palanisamy et al. 2020) as well as the
management overhead of redundant IT (Harris et al. 2012), others, in particular those with limited
resources in the global south (Junglas and Harris 2013) embrace the change and adopt a reversed digital
innovation approach termed the “bottom-up” innovation (Barlette et al. 2021; Junglas et al. 2022). Despite
the diversity of the drivers for CoIT, a common process underlies the phenomenon – individuals’
appropriation of CIT for tasks at the workplace.
Prior research on CoIT and individuals’ digital creativity has focused on investigating the general
antecedents to the dependent variable. Although researchers have recognized the influence of task,
technology, and individual factors as important dimensions of technology use (Junglas et al. 2018; Köffer
et al. 2015; Shao et al. 2021), the uniqueness of contexts warrants further research (Shao et al. 2021).
Furthermore, despite widespread CoIT in the global south (Junglas and Harris 2013), there is limited
research (Agarwal et al. 2016), particularly in Africa and other similar contexts, on how the phenomenon
unfolds under resource constraints. This paper investigates the research question of how CoIT fuels
employee-driven digital innovation in resource-poor economies through individuals’ digital creativity. To
address the question, the paper; a) presents a brief overview of CoIT, b) discusses the theoretical
background and research gap, c) develops propositions and a research model, and d) highlights the
proposed methodology. Finally, the paper concludes with the theoretical and practical implications of the
current research.
IT Consumerization for Digital Innovation
Thirtieth Americas Conference on Information Systems, Salt Lake City, 2024 2
Literature Review
Overview of CoIT and Theoretical Background
Early research on CoIT has primarily focused on the adoption of “Bring Your Own Devices (BYOD)” or
“Choose Your Own Devices (CYOD)” (Köffer et al. 2015) programs. However, with the advancement of CIT
and the influx of social media and other mobile apps, the literature has extended to study the use of both
personal devices and consumer information systems (Dang-Pham et al. 2019). For individuals, CoIT is
mostly driven by perceived personal benefits and convenience (Weeger et al. 2020), whereas, for
organizations, in particular for those with resource constraints, agility, competitiveness, innovation, and
business continuity are at the forefront of the factors (Chan et al. 2018). Yet, CoIT is also considered a
double-edged sword for organizations. On the one hand, the inevitable “shadow IT practice” (Richter et al.
2019) and the management overhead of redundant IT (Harris et al. 2012) pose security challenges, on the
other hand, the versatility of CIT promises a digital innovation potential – making the research on the
phenomenon a worthwhile effort.
Focusing on continued system use rather than mere adoption, the study of individual IT use behavior at the
workplace has become significant and complex, since the collective-level IT use processes are the logical
outcomes of individual-level technology use processes (Nan 2011). Similarly, employee-driven digital
innovation can be enabled through the digital creativity of frontline employees (Opland et al. 2022; Shao et
al. 2021). The vehicle for such digital innovation is individuals’ digital ambidexterity (Hund et al., 2021) –
which is the ability to explore and experiment with digital technologies without putting organizational tasks
in jeopardy. Hence, actively appropriating technology is a recognized saliency of technology use practices.
Hence, adaptive system use, with continuous revision of both the features and the spirit of a technology-in-
use (Sun et al. 2019), has become an important agenda for research and practice alike. Using the role of
both human and material agencies (of technologies) to explain the mutual shaping of technology and
organizational task structures (Schmitz et al. 2016), adaptive structuration theory for individuals (ASTI), is
a viable theoretical lens to study technology-in-use, such as CoIT (Kabanda and Brown 2014). The extended
theory of diffusion of innovations (DOI) on frugal ICTs (Zhang 2018), on its part, through the measures of
adoptability (i.e. relative advantage, compatibility, complexity, trialability, and observability), can help us
explain how individuals identify, appraise, and adopt CITs for organizational task routines.
However, adaptive structuration underlies the process of how the adopted technologies become adapted
and embedded in existing organizational practices and social norms. Drawing on the tripartite view of
technology use, ASTI (Schmitz et al. 2016) can be complemented with DOI (Olsson and Russo 2004) to
study the nuances of how individual digital innovative behaviors impact existing work systems and social
structures through the understanding of socio-technical digital innovations. Thus, this study uses ASTI’s
P3b
Technology
Task
P2
P3a
P1
P8
P7
P6
P4
P5
Explorative
CoIT
EMPwIT
TM
DK
Exploitative
CoIT
Digital
Creativity
(Dis)satisfaction with enterprise IT
BYOD/CYOD policy void
Task Environment Constraints
TDA
TV
Individual
Control Variables
Age, Gender, Education Level,
Work experience
Figure 1. Proposed research model of CoIT adaptation behavior and digital
creativity in a resource-constrained context (adapted from Shao et al., 2021)
EMPwIT
=Empowerment with IT,
DK
= Digital Knowledge,
TM
= Technology Malleability,
TDA
=Technology
Digital Affordance, TV = Task Variety
IT Consumerization for Digital Innovation
Thirtieth Americas Conference on Information Systems, Salt Lake City, 2024 3
“input-process-output” framework (Schmitz et al. 2016), to extend the research model by Shao et al. (2021)
with two moderator variables on resource constraints in the task environment, namely: Dissatisfaction with
enterprise IT and BYOD/CYOD policy void (Junglas et al. 2018). We also add individuals’ empowerment
with IT (Junglas et al. 2022; Junglas and Harris 2013) and technology malleability (Nelson and Ghods
1998) as structuration inputs (antecedents) that influence the structuration process (exploitation and
exploration). Finally, the output is digital creativity. We also control for individuals' age, education levels,
work experience, and gender (See Figure -1 above). Next, we present our propositions.
Individuals’ Ambidexterity and Digital Creativity
Digital creativity is digital innovativeness at the individual level that is primarily affected by the individual’s
digital ambidexterity, i.e., the exploitative and explorative technology use behavior (Shao et al. 2021).
Schmitz et al. (2016) conceptualize this as the structuration process that incumbent structures go through
to generate a desired output – in this case, digital creativity. This ambidexterity enables bottom-up digital
innovations (Hund et al. 2021). Explorative technology use is characterized by careful experimentation of
technology ( both features or spirits of use) to identify new potentials of use in an environment (Chan et al.
2018). In contrast, exploitative technology use is characterized by the routine use of recognized technology
features (Koo et al. 2015; Sun et al. 2019). These use behaviors are complementary to each other (Sun et al.
2019) and have a positive impact on the digital creativity of individuals (Shao et al. 2021). Hence, we
propose; P1: Exploitative CoIT positively influences individual digital creativity. P2: Explorative CoIT
positively influences individual digital creativity.
Bottom-up Digital Innovation and Resource Constraints
Under-resourced environments are more predisposed to disruptive digital innovations than contexts with
established decision procedures and slack resources (Chan et al. 2018). Researchers have found a close,
positive relationship between resource constraints and innovations (Gibbert and Scranton 2013; Keupp and
Gassmann 2013) often termed “necessity-driven innovation”. In a similar vein, the study by Senyard et al.
(2013) showed how actors in resource-poor environments can use a bricolage of resources at hand to
perform workplace tasks. Hence, dissatisfaction with (possibly the absence of) resources leads users to look
for alternative technologies (Junglas et al. 2018) such as CITs which are assumed to have a comparative
advantage (Zhang 2018). Furthermore, workers in emerging economies feel more empowered and
innovative with CIT at the workplace than those in developed economies (Junglas and Harris 2013) partly
because they use their own devices and systems in the absence of alternative company resources. Further,
the absence of explicit rules and regulations – a.k.a. policy voids (Gibbert and Scranton 2013) are
considered other types of task environment constraints. For instance, BYOD or CYOD policy voids can be
taken as opportunities, contingent on the perception of CIT permissibility (Junglas et al. 2018), to flexibly
try alternative solutions without the fear of repercussions. In a resource-poor context, we expect an
increased level of perception of CoIT permissibility, due to the lack of explicit BYOD policy (Kabanda and
Brown 2014). Hence, we propose; P3a: The impact of exploitative CoIT on an individual’s digital
creativity is positively moderated by a) dissatisfaction with enterprise IT and b) the BYOD/CYOD policy
void. P3b: The impact of explorative CoIT on an individual’s digital creativity is positively moderated by
a) dissatisfaction with enterprise IT and b) the BYOD/CYOD policy void.
Individual Characteristics and Appropriation Behaviors
Individuals’ empowerment with IT impacts their digital innovativeness with CoIT. It captures the level of
authority and psychological belief employees assume in utilizing IT to control or shape the conditions of
their tasks and environments (Junglas et al. 2018; Junglas et al. 2022). Further, employees in emerging
markets have higher levels of empowerment with IT (Junglas and Harris 2013). This could be attributed to
the higher levels of adoptability features in CIT, particularly of frugal ICT (Zhang 2018) in emerging
markets. Further, the capability to learn new skills and digital knowledge plays an important role in
developing innovative behavior with IT. Mueller et al. (2016) using the phases of social cognitive theory,
have shown that innovative behavior with IT develops through social interaction. Digital skills acquired via
extensive engagement with social media and other interactive systems can be leveraged to innovate with IT
in the workplace (Shao et al. 2021) – thus, the impetus for digital creativity. Moreover, the actions of
internal and external network actors (Zhang 2018), such as lead users, and peers influence the rate of
IT Consumerization for Digital Innovation
Thirtieth Americas Conference on Information Systems, Salt Lake City, 2024 4
learning new skills for CIT use. Therefore, we suggest; P4: An increased level of individual empowerment
with IT results in a) increased explorative CoIT and b) increased exploitative CoIT. P5: An increased level
of digital knowledge results in a) increased explorative CoIT and b) increased exploitative CoIT.
Technology Characteristics and Appropriation Behaviors
Schmitz et al. (2016) described the malleability of IT as “editable, fluid, interactive, open, reprogrammable,
pliable, and transfigurable” (p.664). Flexibility underlies the malleability of IT (Nan 2011). However, the
flexibility is dependent on the versatility of both structures ( e.g. design and organization) and processes
(e.g. usage procedures) (Nelson and Ghods 1998). Moreover, the traditional measures of adoptability for
CIT, as articulated in DOI (Olsson and Russo 2004), increases the chance of trying and fitting these
technologies in different use contexts. Therefore, both explorative and exploitative CoIT are enhanced by
the malleability of CIT. Further, technology’s digital affordance (TDA) enables the explorative and
exploitative technology use behaviors (Shao et al. 2021). TDA is the action possibilities a technology offers
for a goal-oriented actor (Gaver 1991), or the perceived digitalization functionality (Shao et al. 2021) that
empowers users to complete their tasks. Hence, when technology's affordance matches the intended user
actions, users are encouraged to explore and exploit it further. In this regard, we differentiate between
affordance and malleability of technology in that the latter enables users to create emergent technology
affordances that empower users to perceive new action possibilities (Schmitz et al. 2016). Accordingly, we
propose; P6: Malleability of CIT positively influences a) explorative CoIT and b) exploitative CoIT. P7:
The TDA of CIT positively influences a) explorative CoIT and b) exploitative CoIT
Task Variety and Appropriation Behaviors
Task variety is considered to be an important predictor of both explorative and exploitative technology use
behaviors (Shao et al. 2021). CITs are designed to be versatile which gives individuals the impetus to use
them for various tasks. Further, in resource-constrained environments, the chances of individuals being
assigned to handle a wide variety of tasks at the workplace are expectedly high because having specialized
expertise for every task is impractical due to scarce resources. Hence, we propose; that P8: Task Variety
has a positive influence on employees' a) exploitative CoIT and b) explorative CoIT.
Methodology
In this research, we intend to employ a positivist quantitative survey method to collect data in support of
our propositions. Drawing our sample from employees of over 40 tech start-ups in Ethiopia with various
constraints including infrastructure, policies, and bureaucracy (Desta 2018), we measure the use of
smartphones (both the access point and consumer apps) for workplace tasks. For the data analysis, we plan
to use structural equation modeling (SEM). We also plan to qualitatively complement the result of the SEM
analysis with a fuzzy-set qualitative case analysis (fsQCA) (Pappas and Woodside 2021).
Conclusion
This paper attempted to investigate how CoIT in resource-constrained contexts could contribute to
fostering employee-driven digital innovation in emerging economies. Drawing on the theories of DOI and
ASTI, a comprehensive research model with the moderating role of resource constraints on individuals’
digital creativity is presented. Our contribution is twofold: conceptualizing the positive moderation of
resource constraints on digital creativity through CoIT and the combination of ASTI with DOI to explain
the CoIT phenomenon in resource-poor contexts. Practically, this research underscores the importance of
recognizing and effectively managing CoIT in emerging economies to digitally transform under-resourced
work systems, such as those in SMEs. Future research can test this research model with empirical data to
build evidence of a bottom-up digital innovation driven by CoIT. Future research can also explore the
interplay of sociocultural factors with IT consumerization in resource-poor contexts.
REFERENCES
Chan, C.M.L., Teoh, S.Y., Yeow, A., and Pan, G. 2018. "Agility in Responding to Disruptive Digital
Innovation: Case Study of an SME," Information Systems Journal (29:2), pp. 436-455.
IT Consumerization for Digital Innovation
Thirtieth Americas Conference on Information Systems, Salt Lake City, 2024 5
Dang-Pham, D., Pittayachawan, S., Bruno, V., and Kautz, K. 2019. "Investigating the Diffusion of It
Consumerization in the Workplace: A Case Study Using Social Network Analysis," Information
Systems Frontiers (21:4), pp. 941-955.
Desta, T. 2018. "ICT Innovations, Entrepreneurship and Hubs in East Africa: The Case of Ethiopia," African
Journal of Science, Technology, Innovation and Development (10:6), pp. 655-664.
Gaver, W.W. 1991. "Technology Affordances," Proceedings of the SIGCHI conference on Human factors in
computing systems, pp. 79-84.
Gibbert, M., and Scranton, P. 2013. "Constraints as Sources of Radical Innovation? Insights from Jet
Propulsion Development," Management & Organizational History (4:4), pp. 385-399.
Harris, J., Ives, B., and Junglas, I. 2012. "It Consumerization: When Gadgets Turn into Enterprise It Tools,"
Mis Quarterly Executive (11:3), pp. 99-112.
Hund, A., Wagner, H.-T., Beimborn, D., and Weitzel, T. 2021. "Digital Innovation: Review and Novel
Perspective," The Journal of Strategic Information Systems (30:4).
Junglas, I., Goel, L., Ives, B., and Harris, J. 2018. "Innovation at Work: The Relative Advantage of Using
Consumer It in the Workplace," Information Systems Journal (29:2), pp. 317-339.
Junglas, I., Goel, L., Rehm, S.-V., and Ives, B. 2022. "On the Benefits of Consumer It in the Workplace—an
It Empowerment Perspective," International Journal of Information Management (64), p. 102478.
Junglas, I., and Harris, J. 2013. "The Promise of Consumer Technologies in Emerging Markets,"
Communications of the ACM (56:5), pp. 84-90.
Kabanda, S., and Brown, I. 2014. "Bring-Your-Own-Device (BYOD) Practices in SMEs in Developing
Countries–the Case of Tanzania," Australasian Conference on Information Systems, Auckland, New
Zealand: ACIS.
Keupp, M.M., and Gassmann, O. 2013. "Resource Constraints as Triggers of Radical Innovation:
Longitudinal Evidence from the Manufacturing Sector," Research Policy (42:8), pp. 1457-1468.
Köffer, S., Ortbach, K., Junglas, I., Niehaves, B., and Harris, J. 2015. "Innovation through Byod?," Business
& Information Systems Engineering (57:6), pp. 363-375.
Koo, C., Chung, N., and Kim, H.-W. 2015. "Examining Explorative and Exploitative Uses of Smartphones:
A User Competence Perspective," Information Technology & People (28:1), pp. 133-162.
Nan, N. 2011. "Capturing Bottom-up Information Technology Use Processes: A Complex Adaptive Systems
Model," Mis Quarterly (53:2), pp. 505-532.
Nelson, K.M., and Ghods, M. 1998. "Measuring Technology Flexibility," European Journal of Information
Systems (7:4), pp. 221 - 231.
Olsson, C.M., and Russo, N.L. 2004. "Evaluating Innovative Prototypes," Boston, MA: Springer US, pp. 181-
202.
Opland, L.E., Pappas, I.O., Engesmo, J., and Jaccheri, L. 2022. "Employee-Driven Digital Innovation: A
Systematic Review and a Research Agenda," Journal of Business Research (143), pp. 255-271.
Palanisamy, R., Norman, A.A., and Mat Kiah, M.L. 2020. "BYOD Policy Compliance: Risks and Strategies
in Organizations," Journal of Computer Information Systems (62:1), pp. 61-72.
Pappas, I.O., and Woodside, A.G. 2021. "Fuzzy-Set Qualitative Comparative Analysis (Fsqca): Guidelines
for Research Practice in Information Systems and Marketing," International Journal of Information
Management (58).
Richter, S., Waizenegger, L., Steinhueser, M., and Richter, A. 2019. "Knowledge Management in the Dark:
The Role of Shadow It in Practices in Manufacturing," International Journal of Knowledge
Management (IJKM) (15:2), pp. 1-19.
Schmitz, K.W., Teng, J.T., and Webb, K.J. 2016. "Capturing the Complexity of Malleable It Use: Adaptive
Structuration Theory for Individuals," Mis Quarterly (40:3), pp. 663-686.
Shao, Z., Li, X., and Wang, Q. 2021. "From Ambidextrous Learning to Digital Creativity: An Integrative
Theoretical Framework," Information Systems Journal (32:3), pp. 544-572.
Sun, H., Wright, R.T., and Thatcher, J.B. 2019. "Revisiting the Impact of System Use on Task Performance:
An Exploitative-Explorative System Use Framework," JAIS (20:4).
Weeger, A., Wang, X., Gewald, H., Raisinghani, M., Sanchez, O., Grant, G., and Pittayachawan, S. 2020.
"Determinants of Intention to Participate in Corporate Byod-Programs: The Case of Digital Natives,"
Information Systems Frontiers (22), pp. 203-219.
Zhang, X. 2018. "Frugal Innovation and the Digital Divide: Developing an Extended Model of the Diffusion
of Innovations," International Journal of Innovation Studies (2:2), pp. 53-64.
