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Corresponding author: Josephat Deusidedith Sengura
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The influence of business networks on frugal innovation capability: The role of
organizational ambidexterity
Josephat Deusidedith Sengura 1, 2, *, Renyan Mu 1, 3 and Jacobo Busumabu 2, 4
1 School of Management, Wuhan University of Technology, Wuhan 430070, China.
2 Department of Applied Science and Social Studies, Arusha Technical College, Arusha P.O. Box 296, Tanzania.
3 Center of Production Innovation Management, Hubei Province, Wuhan 430070, China.
4 Xi’an Jiaotong University, Shaanxi Province, Xi’an 710049, China.
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
Publication history: Received on 19 July 2024; revised on 28 August 2024; accepted on 30 August 2024
Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2637
Abstract
This study explores the role of business networks—specifically, inter-firm collaboration (IFC), university relationships
(UR), and government relationships (GR)—in shaping frugal innovation capability (FIC) among small and medium-sized
enterprises (SMEs) in emerging and developing economies (EMDEs). Based in the resource-based view (RBV) theory
and dynamic capability theory (DCT), the study expands on existing literature by investigating how these networks
influence FIC and the moderating role of organizational ambidexterity (OA) in these relationships. The empirical
findings confirm that IFC and GR significantly enhance FIC, whereas UR does not have a substantial impact, likely due
to a misalignment between academic research and the practical needs of SMEs. Notably, OA negatively moderates the
IFC-FIC relationship, indicating that high OA might introduce complexities that hinder the benefits of IFC on FIC. In
contrast, OA positively moderates the GR-FIC relationship, suggesting that the effectiveness of government support in
promoting frugal innovation is amplified when SMEs possess high levels of OA. The findings highlight the importance of
targeted business networks and the nuanced role of OA in maximizing FIC in resource-constrained environments. This
research contributes to the RBV literature by demonstrating the differential impacts of various business networks on
FIC and highlighting the critical moderating role of OA.
Keywords: Business networks; Frugal innovation; Frugal innovation capability; Organizational ambidexterity;
Manufacturing SMEs; Emerging market and developing economies
1. Introduction
In today's business environment, where technological progress is swift and global competition is intense, the ability to
innovate frugally has emerged as a vital strategic capability for firms, especially in resource-constrained environments
(Cai et al., 2019; Santos et al., 2020). Recently, there has been a major increase in scholarly interest in frugal innovation
(FI), which is the process of developing affordable and resource-efficient goods and services that are customized to meet
the requirements of low-income populations (3–6). It also involves the process of minimizing the intricacy and expense
of a service or a good, making it particularly relevant for Small and Medium-sized Enterprises (SMEs) in emerging
market and developing economies (EMDEs), such as Tanzania (7,8). These firms often face significant resource
limitations, necessitating innovative approaches to remain competitive and sustainable (9). Additionally, they must
meet customer demands, which are typically focused on affordability and are highly sensitive to prices (1). In the
contemporary, rapidly evolving environment, the ability of SMEs to thrive is predominantly dependent on their capacity
for innovation, which allows them to respond effectively to the rapid changes in the marketplace (10,11). As a result,
both practitioners and researchers are continually investigating and identifying ways to enhance the innovation
capacity of business organizations.
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
105
Therefore, to successfully implement frugal innovation, SMEs in EMDEs must develop particular capabilities due to its
unique approach, which diverges from conventional innovation methods (12). Given the unique attributes of frugal
innovation, it is recommended that SMEs must develop specialized capabilities to foster its advancement (13). The
current literature predominantly views this process as driven by grassroots initiatives (14), rather than as a more
widespread application across the firm. Additionally, Brem et al. (14) did not attempt to ascertain the specific
capabilities required for the development of FI. Although there is research addressing FI, including studies on
capabilities (15) and resources (16), there remains a gap in understanding how to develop these capabilities within
firms. Furthermore, the SME sector in EMDEs, such as Tanzania, has insufficient research to demonstrate how these
enterprises develop frugal innovation capability (FIC) within the resource constrained environment. Studies at the
micro-level processes of business innovation highlights that firms rely on and utilize external resources, such as
business networks, for their innovation and development efforts (17–19). Business networks are pivotal for SMEs, as
they provide access to essential resources, knowledge, and opportunities that might otherwise be beyond the reach of
individual firms. Through collaboration with other businesses, suppliers, customers, and various stakeholders, SMEs
can leverage external expertise and capabilities, fostering an environment conducive to innovation (20). In the context
of FI, business networks can offer critical support by facilitating the exchange of cost-effective solutions, sharing best
practices, and enabling joint problem-solving efforts (21). This collaborative dynamic can significantly enhance an
SME's ability to innovate under resource constraints, thereby improving its competitiveness and sustainability.
Similarly, organizational ambidexterity (OA)—the ability of a firm to balance exploration and exploitation activities—
is considered a critical internal capability (22). This capability fosters firms' innovation capabilities in emerging markets
(8). Exploration entails the pursuit of new knowledge and opportunities, whereas exploitation is concerned with
improving existing capabilities and optimizing the use of resource (23). For SMEs in EMDEs, achieving ambidexterity is
essential to navigate the complexities of frugal innovation (8). By balancing these dual activities, firms can effectively
harness their business networks to innovate efficiently and sustainably. Ambidextrous organizations are better
positioned to integrate external knowledge with internal capabilities, thereby enhancing their frugal innovation
outcomes (22). Despite the recognized importance of business networks and organizational ambidexterity in fostering
innovation, there is limited empirical research examining their combined impact on FIC in the context of EMDEs. This
study seeks to fill this gap by exploring how business networks influence the frugal innovation capability and how
organizational ambidexterity moderates this relationship with evidence from Tanzanian manufacturing SMEs. The
findings of this study are expected to enrich the current body of knowledge on RBV, DCT, frugal innovation, business
networks, and organizational ambidexterity by offering insights specific to the context of EMDEs manufacturing SMEs.
Moreover, the practical implications of this research can guide policymakers, business leaders, and other stakeholders
in designing strategies and interventions that support the innovative capabilities of SMEs in resource-constrained
environments. By highlighting the critical role of business networks and the importance of fostering organizational
ambidexterity, this study aims to inform efforts to enhance the competitiveness and sustainability of SMEs through
frugal innovation.
2. Literature review and hypotheses development
2.1. Underlying theories
This research draws on two fundamental theories: the “resource-based view (RBV)” and “dynamic capability theory
(DCT)” to explore the connections between the variables. According to the RBV, in order for businesses to accomplish
their objectives, they must be in possession of and able to effectively manage their distinctive assets (24). Barney (25)
stated that these assets might consist of knowledge, capabilities, and resources. The RBV suggests that companies
possessing resources that are “valuable, rare, difficult to imitate, and non-replaceable (VRIN)” can achieve a competitive
edge and earn economic surplus (25). The theory further suggests that firms' ability to control these assets varies, as
rivals will struggle to replicate these critical resources (26). The RBV highlights the importance of both internal and
external resources, including those acquired through business networks, in driving a firm's innovation success (27).
According to Wernerfelt, (28,29) and Barney, (25), the fundamental tenet of the RBV philosophy is that a firm's strength
is derived from its core competences, which give it a long-term competitive advantage via efficient resource
management. However, resource availability is often a weakness for SMEs. SMEs often face challenges related to
finances, size-specific disadvantages, and their ability to fully leverage their strengths. The key aspect of a network lies
in the resources and exchanges among its partners (30). According to Zaheer and Zaheer (31), SMEs may derive
significant benefits from networks only if they select the appropriate network to obtain the essential resources. These
resources will only be beneficial to SMEs and their network partners if they can be effectively utilized to implement
strategies that positively impact performance. The DCT emerged as an extension of the RBV theory (32). According to
Schilke (32) and Helfat and Peteraf (33), while the RBV theory focuses on how a firm's current resources affect its
competitive position, the dynamic capabilities perspective emphasizes the need for reconfiguring existing resources
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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and developing new ones. Moreover, Helfat and Peteraf (33) state that the RBV explains differences among enterprises
based on the resources they possess, which, in turn, affects their competitive advantage.
Therefore, dynamic capabilities are crucial because they enable a firm to modify its resource base, thereby enhancing
its competitive advantage (32,33). DCT delves into how firms can adapt and sustain competitive advantage by
continually modifying their capabilities and resources to meet evolving market demands. This adaptation process
involves crucial elements such as learning, innovation, and strategic decision-making (34), enabling firms to cordinate
resources and cultivate new capabilities to enhance their ability to innovate (35). Moreover, organizational
ambidexterity (OA), which refers to the ability to balance both exploration and exploitation, is recognized as a vital
dynamic capability that impacts innovation (36,37). Exploration involves searching and sensing capabilities, while
exploitation involves selecting and seizing resources and opportunities (36). Balancing these aspects ensures both
immediate performance and long-term innovation. Therefore, organizational ambidexterity strengthens a firm’s
adaptability, innovation capacity, and competitive advantage (38). This study, however, focuses on SMEs and examines
how external resources, such as business networks, and internal capabilities, like organizational ambidexterity,
influence frugal innovation. These capabilities help achieve a sustainable competitive advantage by serving as strategic
resources characterized by their value, rarity, difficulty to replicate, and lack of substitutes, which distinguishes firms
in competitive environments (39).
2.2. Frugal innovation capability
From a theoretical perspective, frugal innovation (FI) remains in its early stages of development (4). However, there
has been a growing interest in this innovation type because of the advantages it provides to organizations, such as
minimizing resource consumption, lowering production expenses, and reducing unnecessary material (40).
Consequently, it has been argued that the marketplaces with limited resources are particularly conducive to fostering
the growth of FI (41). EMDEs are frequently the source of frugal innovation (FI) in situations when resources are limited
(42). Originally, this kind of innovation was developed to give customers who could not otherwise have access to
particular goods or services an inexpensive option to satisfy the specific requirements of these markets (43). This
notion, motivated by the desire to meet the demands of customers from EMDEs, has been present for a considerable
duration (5,44). FI takes place within value chains that are designed to be inclusive, making efficient use of resources to
enhance value and lower costs. This approach transforms limitations into chances for creating creative strategies (4,42).
FI may be identified by four primary attributes: cost-effectiveness, user-friendliness, high efficiency, and sustainability
(45). According to Weyrauch and Herstatt (46), an innovation to be considered as "frugal," if it fulfills three specific
requirements: “substantial cost reduction, a focus on core functionalities, and optimized performance levels”. In a
previous study, Bhatti (47) suggested that FI has the ability to not only improve the design of offerings more effectively,
but also to reorganize value chains and redefine business models. This can ultimately lead to the creation of sustainable
marketplaces that cater to all sorts of consumers. As a result, Pisoni et al. (5) contend that FI is an all-encompassing
technique that spans the entire method of developing creative and cost-effective options in a variety of geographical
locations. These conceptualizations align with the newly suggested theoretical paradigm presented by Rossetto et al.
(13). According to Rossetto et al. (13), FI consists of three dimensions: “focus on core functionalities, shared sustainable
engagement, and substantial cost reduction”. Moreover, FI is not just described in terms of mindset, but also the process
capabilities that allow them to innovate under constraints and turn adversity into growth opportunities (48).
Furthermore, Adler and Shenbar (49) define innovative capability as the capacity to respond to unexpected
opportunities created by a dynamic competitive environment. In order to demonstrate how innovation capability
functions, Sher and Yang (50) underline the significance of volatile circumstances. They claim that companies with this
competence have effectively combined systematically important resources to foster innovation and preserve
competitive advantages (51). Greeven (52) provides a comprehensive definition of innovation capability that includes
all of the previously mentioned aspects. It is defined as “a firm ability of a firm to integrate, build, and reconfigure
internal and external critical resources to develop and successfully commercialize new products and services”. This
definition is strongly established within the context of DCT. Consistent with this perspective, evidence from enterprises
that operate in emerging markets and developing economies (EMDEs) demonstrates that enterprises actively use frugal
innovation as a strategy to access a burgeoning middle class and tackle the uncertainties of a turbulent economic
environment (4,53).It is also observed that in order to survive in high velocity and uncertain EMDEs, firms have
continuously to change/rejuvenate themselves, a core tenet of dynamic capabilities theory (54). Firms that want to
exploit opportunities in EMs recognize that the dynamic capabilities required in the present context differ from those
cultivated in previous times (Eisenhardt and Martin, 2000). Thus, this paper leverages the advances in RBV and DCT to
empirically validate the influence of business networks in developing frugal innovation capability in EMDEs moderated
by organizational ambidexterity with evidence from Tanzania manufacturing SMEs.
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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2.3. Business networks and frugal innovation capability
The notion of a network has been examined from multiple perspectives. It can be viewed as a combination of various
participants and the interconnected network of interactions that link them (55). Or, as Knoke and Kuklinski (56)
described it, a network can be seen as a distinct structure that depicts the connections among a set of individuals, groups,
or events. As a particular kind of network, a business network is characterized as a collection of two or more related
interactions, with each exchange connection connecting commercial enterprises, which are seen as collective
participants in commercial relationships. To put it another way, business networks can be seen as collections of
interconnected enterprises (57) or as collections of interconnected and constantly changing linkages between
enterprises (58). More precisely, a business network is an extended collaboration between two or more firms that
emerge through mechanisms that are different from both market transactions and organizational hierarchies (59).
Powell (60) viewed networks as a transactional intermediary that blends elements of both market and hierarchical
structures, characterized by collaborative conduct. Previous studies have consistently acknowledged the significance of
business networks in facilitating company innovation (61–63) and enhancing company competitive advantage (64).
Through business networks companies may share valuable technical information and engineering expertise inside
networks of social, professional, or transactional ties, giving them a competitive edge (65).
Furthermore, collaborative partnerships can boost R&D innovation (66). In general, the significance of business
networks and collaborations with various entities—including inter-firm collaboration (customers, suppliers and
competitors), universities, public research institutions, as well as government bodies—enables firms to access external
resources (67). Building on this premise, the present study examines the role of business networks as providers of
intangible resources for firms. Numerous research on innovation in SMEs have come to the conclusion that these firms
often struggle with limited financial and specialized human resources, which impedes their innovation capabilities
(68,69). As a result, innovative SMEs are motivated to work with others due to the challenges they face in independently
managing the whole innovation process (70). Different collaboration models, such as networks in cooperation, strategic
alliances and partnerships are influenced by the interaction between different players, their specific responsibilities,
and the intensity of their connections (71). The substantial body of literature on inter-firm collaboration among SMEs,
including supplier–customer–competitor relationships, has been extensively explored (72). SMEs have the opportunity
to enhance their skills by learning from their suppliers, enabling them to capitalize on the combined strengths of their
offerings and technology (73,74). Additionally, SMEs may improve or preserve their competitive advantage and better
position their goods in the market by gathering market-oriented information from customers (75,76). Furthermore,
SMEs can enhance their innovation capabilities by learning from competitors through benchmarking best practices and
forming collaborative networks for shared innovation projects, relationships (73–75).
Building upon this, inter-firm collaboration among SMEs in EMDEs can significantly bolster frugal innovation capability,
especially within resource-constrained environments. Such collaborations enable SMEs to pool their limited resources
and share risks, which is crucial in contexts where financial and material constraints are prevalent (72). By working
together, SMEs can engage in cost-sharing initiatives for research and development, leading to more affordable and
resource-efficient innovations (73,74). Collaborative efforts also facilitate the sharing of tacit knowledge and practical
expertise, which are often pivotal creating cost-effective, valuable solutions that meet specific local market demands
(75). Through strategic partnerships, SMEs can access new markets and distribution channels, thereby enhancing their
ability to scale frugal innovations (74). Based on a comprehensive review of the available literature, the following
hypothesis was developed;
H1a: Inter-firm collaboration positively influences frugal innovation capability
Furthermore, a rising body of academic research and policy publications on innovation has emphasized the importance
of corporate-university partnerships as a means to assure the efficacy of an ecosystem of innovation (73,77).
Collaboration between SMEs and universities, as well as research organizations, significantly enhances innovation
capabilities. Studies show that partnerships with academic institutions and government research institutes positively
impact innovation performance by bridging the gap between innovation creation and commercialization (78).
Additionally, informal interactions between SMEs and universities are found to be more impactful in fostering
successful collaborations and knowledge co-creation, aligning with SMEs' preferences for informal modes of interaction
(79). Public funding programs promoting Industry-University-Research Institute interactions have been effective in
establishing networks that facilitate knowledge flow and dissemination among actors, with universities playing a
central role in these collaborations (80). Additionally, it has been demonstrated that collaboration with a variety of
partners, including universities, rival businesses both domestically and internationally, customers and suppliers,
predicts innovation performance in SMEs, highlighting the significance of varied collaborative relationships for SME
innovation (81,82).
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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Collaboration with universities and research institutions may greatly improve the ability of SMEs in EMDEs to innovate
in a cost-effective manner, particularly in contexts with limited resources. Such partnerships facilitate access to cutting-
edge research, advanced technology, and specialized knowledge that SMEs might otherwise lack (78). By leveraging the
expertise and infrastructure of academic institutions, SMEs can develop cost-effective, sustainable, and innovative
solutions tailored to the specific needs of emerging markets (77). This synergy not only helps in overcoming the
resource limitations but also accelerates the innovation process by combining practical business insights with academic
rigor (73). The mutual exchange of knowledge and resources between SMEs and academic institutions thus creates a
robust framework for frugal innovation, enabling SMEs to thrive in competitive and resource-limited environments
while addressing the unique challenges of emerging markets (81,82). From this information, the following hypothesis
was developed;
H1b: SMEs collaboration with universities and research organizations positively influences frugal innovation capability
Additionally, by establishing suitable legislative frameworks, offering monetary supports, and cultivating an innovation-
friendly climate, governments can enhance the ability of SMEs to develop cost-effective solutions. For instance, policies
that reduce bureaucratic hurdles and improve access to financing can help SMEs allocate their limited resources more
efficiently towards innovative activities. Moreover, government initiatives aimed at enhancing infrastructure, such as
improving internet connectivity and transportation networks, can further support SMEs in overcoming resource
constraints (83). Additionally, public-private partnerships and government-supported training programs may equip
SMEs with the essential expertise and knowledge to leverage frugal innovation strategies effectively (21). Consequently,
an active and supportive governmental role is pivotal in enabling SMEs to thrive and innovate in resource-limited
settings in emerging markets. Therefore, based on this information, the following hypothesis was developed;
H1c: Government role positively influences SMEs frugal innovation capability
2.4. Moderating role of Organizational ambidexterity
Organizational ambidexterity (OA) is the ability of a firm to concurrently engage in both exploitative and exploratory
operations (38,84). There is a general consensus in the body of works a firm with ambidextrous capabilities can
effectively utilize its current strengths while also seeking out new opportunities, leading to improved performance and
competitiveness (85). Researchers in the field of OA suggest that companies can enhance their performance by
simultaneously engaging in both exploitation and exploration (86,87). According to Volberda and Lewin (88),
companies that prioritize exploration activities enhance their ability for updating their knowledge pool. However, a
disproportionate focus on exploration might result in a never-ending cycle of searching and implementing futile
improvements. In order to prevent this issue, companies must also engage in exploitation activities. While effective
exploitation is crucial for maintaining a company’s present viability, engaging adequately in exploration is vital for
securing its future sustainability. Previous studies (89,90) suggested that in order for businesses to achieve innovative
synergy capabilities, they must maintain optimal equilibrium between exploitation and exploration in their inventive
operations. The beneficial influence of OA on a company’s effectiveness is widely acknowledged from a balanced
viewpoint (91,92). Studies conducted earlier highlight the positive outcomes of incorporating both exploitation and
exploration capabilities into the overall dimension. Companies may improve their business learning ability and
effectiveness by utilizing the combined dimension, which enables them to successfully harness both exploitation and
exploration capabilities (93). Furthermore, ambidexterity—which effectively blends the two different strategic
philosophies of exploration and exploitation—allows businesses to realize their dynamic capabilities (94). The
ambidexterity which provides for this integration improves businesses' innovation strategy.
The underlying assumption of the aforementioned main argument is that exploitation and exploration work well
together to provide a matching strategy that increases the effectiveness of innovation methods (95). OA places a strong
emphasis on striking a balance between the exploitation and exploration orientations in order to accomplish innovation
deployment; this balance should improve the innovation results of organizations. OA benefits businesses, and as a result,
these businesses are more likely to search for and obtain the necessary resources to handle the demand for internal
innovation implementation and carry out ongoing innovation initiatives. Recent research has explored OA within
moderated models. For instance, OA serves as a moderator in the association between green supplier integration and
business performance (96). According to Roldán Bravo et al. (97), the link between a purchasing organization's supply
chain competency and its desorptive capacity was found to be moderated by organizational ambidexterity. In order to
solve their internal resource limitations and enhance their innovation ability, organizations need external innovation
resources like business networks. Additionally, they need be driven to pursue new possibilities (98).
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
109
To extend the discussion on organizational ambidexterity, it is crucial to consider its moderating role. In the context of
SMEs operating in EMDEs, organizational ambidexterity can serve as a critical moderating factor between business
networks and the development of FIC. These SMEs often face significant resource constraints and operate in volatile
environments, making the balance between exploitation and exploration crucial for their sustainability and growth. By
leveraging organizational ambidexterity, these firms can effectively utilize their business networks to acquire external
knowledge and resources while simultaneously refining and exploiting their existing capabilities (38,84). This dual
approach allows SMEs to innovate in a cost-effective manner, aligning with the principles of frugal innovation. The
integration of external insights from business networks with internal capabilities through an ambidextrous approach
enables these firms to develop innovative solutions that are not only resource-efficient but also tailored to the unique
challenges of emerging markets. Therefore, organizational ambidexterity leads to stronger competitive advantages in
resource-constrained situations by optimizing the value generated from business networks and enhancing SMEs' ability
to innovate (99). Hence, we propose the following hypothesis:
H2a: Organizational ambidexterity positively moderates the relationship between inter-firm collaboration and frugal
innovation capability
H2b: Organizational ambidexterity positively moderates the relationship between SMEs collaboration with university and
research and frugal innovation capability
H2b: Organizational ambidexterity positively moderates the relationship between Government role and frugal innovation
capability
Figure 1 Illustrates the theoretical model for this study, including each of the proposed hypotheses.
3. Methods
3.1. Sample and data collection
Figure 1: The theoretical model
This study gathered survey data from manufacturing SMEs located in Dar es Salaam and Arusha, two key Tanzanian
cities known for their significant roles in the nation’s manufacturing industry (100). The study focused on
manufacturing SMEs within three specific sub-sectors: furniture (including products made from metals and plastics),
fashion (which covers apparel, shoes and clothing) and food (which includes food processing and beverages). These
SMEs were required to have been operational for at least three years. Quaye et al. (101) suggest that a period of three
years is considered sufficient to assess the growth and success of a business. The decision to focus on these sub-sectors
was influenced by their strong presence among Tanzanian manufacturing SMEs (102). The unit of analysis in this
Business networks Moderator
H2c
H2b
H2a
H1c
H1b
H1a
Inter-firm
collaboration
(IFC)
University and
research
organization
(UR)
Government
role (GR)
Frugal
innovation
capability (FIC)
Organizational
ambidexterity
(OA)
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
110
research corresponds with the Tanzanian definition of SMEs, which categorizes them as enterprises employing up to
100 people (103). Our sampling frame was developed using a comprehensive list of all SMEs, sourced from the “Business
Registration and Licensing Agency (BRELA)”, and further validated by an official list acquired from “small industry
development organizations (SIDO)” in the respective regions. We began by reaching out to the administrative
departments of the relevant SMEs using multiple methods, including personal visits, phone calls, or email to seek
permission for their participation in the study. A pre-test survey was carried out and refined prior to distributing the
final questionnaire. In total, 845 questionnaires were sent via email, with a request for completion by key decision-
makers such as “CEOs, owners, general managers, marketing managers, production and operations managers, finance
managers, and human resources managers”. We received a total of 663 completed questionnaires, of which 84 were
invalid due to incomplete responses, resulting in a valid response rate of 69%. The survey was carried out over
approximately four months, was completed in August 2023. To enhance the response rate, phone calls and follow-up
emails were made throughout the data collection period. In order to assess the possibility of non-response bias, we
conducted a comparison between the replies of early respondents (those who completed the survey within three
months) and late respondents (those who answered after three months or after reminders). Non-response bias is
unlikely to be a problem in this research, given no significant differences were seen between these two groups on
important variables (104).
3.2. Measurement scales
This study utilizes scales generated from existing empirical research. The study used five-point Likert scales, with a
rating of 1 indicating strongly agree and a rating of 5 indicating strongly disagree.
3.2.1. Business networks
The construct of business networks was evaluated using a scale that included three main dimensions: inter-firm
collaboration (IFC), adapted from Huang et al. (105) and Gemünden et al. (106); university and research organization
(UR), based on the work of Orozco and Ruiz (107); and the government's role (GR), as defined by Shou et al. (108) and
Mondejar and Zhao (109). The scale consisted of 12 items for IFC, 14 for UR, and 10 for GR.
3.2.2. Organizational ambidexterity
The construct of organizational ambidexterity (OA) was assessed using a scale that comprised two dimensions:
exploratory and exploitative. SMEs in EMDE can develop dynamic and resilient innovation capabilities by
simultaneously engaging in both exploratory and exploitative activities. The OA construct was evaluated using a 12-
item scale, with 6 items dedicated to exploratory activities and 6 items focused on exploitative activities, based on the
work of Lubatkin et al. (110).
3.2.3 Frugal innovation capability
Frugal innovation capability (FIC), a second-order construct, was evaluated using a scale for frugal innovation that
encompasses three dimensions: focus on core functionalities (FCF) with 3 items, substantial cost reduction (SCR) with
4 items, and sustainable co-creation (SCC) with 3 items. The measurement scale for FIC was adapted from Santos et al.
(42) because it is well-suited to the requirements for measuring FIC and covers the essential aspects needed for this
study.
3.2.3. Data analysis methods and Common method bias
The analysis utilized structural equation modeling (SEM) to evaluate how well the constructs within the data aligned
with the proposed model. This was conducted using AMOS 29 software. Additionally, SmartPLS4 was employed to
assess the reliability and validity of the measurements, as well as to test the hypothesized relationships between the
research constructs through structural model assessment. We utilized Podsakoff et al. (111)'s single-factor Harman test
to evaluate the possibility of common method bias. We performed exploratory factor analysis (EFA) on all self-reported
measures, employing principal factoring with varimax rotation. Seven variables with eigenvalues greater than one were
found in this study, and they collectively explained 64.98% of the variation. The first factor accounted for 31.49% of the
variation, which falls below the 50% criterion. This indicates that common method bias is not a major problem in the
present study.
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4. Results
4.1. Demographic characteristics
The demographic variables examined in this study include gender, respondents' years of service within their current
SMEs, job designation, SMEs sub-sector within the manufacturing industry, the age of the SMEs since establishment,
and their geographical location. Among the respondents, 64.1% were male, while 35.9% were female. Regarding tenure,
28.67% of respondents have been with their current SMEs for 1 to 3 years, 54.58% for 4 to 6 years, and 16.75% for
more than 6 years. In terms of job roles, 40.41% of respondents held positions as Owner, CEO, or General Manager,
10.19% as Finance Manager, 19.52% as Marketing Manager, 8.64% as HR Manager, and 21.24% as Production or
Operations Manager. The SMEs in the manufacturing sector were distributed across various sub-sectors, with 33.85%
in the fashion sub-sector (e.g., textiles, clothing, footwear, leather goods, cosmetics, and soap, jewelry), 40.76% in the
food sub-sector (e.g., food processing, beverages, dairy products), and 25.39% in the furniture, fittings, plastics, and
metals sub-sector (including lighting articles and appliances, chemicals, and rubber products). The majority of the SMEs
surveyed (47.67%) have been established for 5 to 10 years, with 28.50% having been established for less than 5 years
and 23.83% for more than 10 years. Geographically, most of the SMEs participating in this study were located in Dar es
Salaam (64.08%), followed by Arusha (35.92%). The demographic profile of the study's respondents is detailed in Table
1..
Table 1 Demographic characteristics of Manufacturing SMEs
Demographic variables
Frequency
Percentage
Gender
Male
371
64.08
Female
208
35.92
Total
579
100.00
Years of services in the current organization
1 to 3 years
166
28.67
4 to 6 years
316
54.58
More than 6 years
97
16.75
Total
579
100.00
Designation
Owner/CEO/General Manager
234
40.41
Finance Manager
59
10.19
Marketing Manager
113
19.52
HR Manager
50
8.64
Production/ Operations Manager
123
21.24
Total
579
100.00
Firm sub-sector in manufacturing industry
Fashion (e.g., Textile/ clothing, footwear, leather goods, cosmetics & soap, jewelry)
196
33.85
Food (e.g., Food processing, alcoholic & non-alcoholic beverage, dairy products)
236
40.76
Furniture, fittings, plastic & metals (including lighting articles & appliances,
chemicals, rubber products).
147
25.39
Total
579
100.00
SMEs age since establishment
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
112
Below 5
165
28.50
Between 5 to 10
276
47.67
Above 10
138
23.83
Total
579
100.00
Firm location
Dar es Salaam city
371
64.08
Arusha city
208
35.92
Total
579
100.00
4.2. Measurement model
In order to ascertain if the model adequately fits the gathered data, we first used AMOS 29 software to perform
confirmatory factor analysis (CFA). The fit statistics demonstrate excellent match between the model and the data, as
evidenced by a chi-square (X2) to degrees of freedom (df) ratio (CMIN/DF) of 1.900, where the X2 value is 2990.404 and
the df is 1574. The measurement model also includes the following fit indices: GFI = 0.842, RMSEA = 0.039, IFI = 0.940,
NFI = 0.907, AGFI = 0.828, and CFI = 0.939. All fit indices, as shown in Table 2, are within an acceptable range, indicating
a good fit between the model and the data (112,113). Moreover, SmartPLS4 software (114) was employed to assess the
measurement model's validity and reliability. Composite reliability (CR) and Cronbach's alpha were used to assess the
theoretical constructs' internal consistency. According to Cronbach's (115) criterion, all of the constructs as presented
in Table 3 have Cronbach's alpha values more than 0.7, which indicates good internal consistency and reliability of the
constructs. Similarly, all constructs' composite reliability (CR) scores were higher than the suggested cutoff of 0.7, which
further validates the measuring scales employed in the research (116). In addition, the factor loadings for each item
were above the acceptable value of 0.5, demonstrating an adequate level of reliability (116). Furthermore, Table 3
shows that all constructs' average variance extracted (AVE) values were higher than the suggested cutoff point of 0.5,
thereby confirming convergent validity (116). We computed the heterotrait-monotrait ratio of correlations (HTMT) in
accordance with Henseler et al. (117)'s guidelines to evaluate discriminant validity. All HTMT values were below the
suggested cutoff of 0.90 (118), demonstrating discriminant validity, as indicated by the results displayed in Table 4.
Moreover, we examined the variance inflation factor (VIF) (refer Table 3), all indicators and constructs reporting values
below 0.5, indicating no significant multicollinearity. Thus, the constructs in this study satisfy the criteria necessary for
conducting structural analysis.
Table 2 The fit indices of the CFA model
Fit index
Scores
Proposal threshold criteria
Absolute fit measures
CMIN/df (Chi-square/df)
1.900
≤2a; ≤ 5b
GFI (goodness of fit index)
0.842
≥0.90a; ≥ 0.80b
RMSEA (root mean square error of approximation)
0.039
≤0.08a; ≤ 0.10b
Incremental fit measures
IFI (Incremental fit index)
0.940
≥0.90a
NFI (incremental fit measures including normed fit index)
0.907
≥0.90a
AGFI (adjusted goodness of fit index)
0.828
≥0.90a; ≥ 0.80b
CFI (comparative fit index)
0.939
≥0.90a
Note(s): a: Good fit; b: Acceptable fit (112,113)
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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Table 3 Results of the measurement model, including factor loadings, reliability indices, validity assessments, and
variance inflation factors (VIF)
Construct
Item
Outer
loading
Cronbach's alpha
Composite reliability
AVE
Collinearity
statistics (VIF)
Inter-firm
collaboration
IFC1
0.830
0.948
0.954
0.634
2.914
IFC2
0.836
3.010
IFC3
0.790
2.623
IFC4
0.763
2.298
IFC5
0.801
2.886
IFC6
0.763
2.161
IFC7
0.820
2.839
IFC8
0.787
2.369
IFC9
0.794
2.447
IFC10
0.804
2.598
IFC11
0.776
2.638
IFC12
0.788
2.787
University and
research
organization
UR1
0.782
0.946
0.952
0.586
2.394
UR2
0.813
2.625
UR3
0.755
2.088
UR4
0.761
2.146
UR5
0.757
2.162
UR6
0.738
2.008
UR7
0.754
2.089
UR8
0.758
2.143
UR9
0.765
2.181
UR10
0.765
2.207
UR11
0.756
2.048
UR12
0.766
2.004
UR13
0.761
2.261
UR14
0.785
2.238
Government role
GR1
0.813
0.936
0.946
0.636
2.550
GR2
0.715
2.368
GR3
0.799
2.603
GR4
0.817
2.899
GR5
0.825
2.129
GR6
0.764
2.708
GR7
0.804
2.555
GR8
0.798
2.623
GR9
0.824
2.597
GR10
0.812
1.758
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114
Organizational
ambidexterity
EXPL1
0.765
0.936
0.945
0.588
2.222
EXPL2
0.778
2.435
EXPL3
0.780
2.352
EXPL4
0.762
2.184
EXPL5
0.787
2.867
EXPL6
0.804
2.920
EXPR1
0.747
2.091
EXPR2
0.764
2.183
EXPR3
0.765
2.200
EXPR4
0.757
2.122
EXPR5
0.736
1.996
EXPR6
0.755
2.144
Focus on core
functionality
FCF1
0.871
0.841
0.904
0.759
2.011
FCF2
0.869
1.957
FCF3
0.874
2.011
Sustainable co-
creation
SCC1
0.919
0.907
0.942
0.843
3.007
SCC2
0.907
2.740
SCC3
0.929
3.288
Substantial cost
reduction
SCR1
0.900
0.902
0.932
0.774
3.335
SCR2
0.838
2.004
SCR3
0.904
3.380
SCR4
0.875
2.553
Frugal innovation
capability
FCF
0.862
0.722
0.844
0.644
1.623
SCC
0.784
1.407
SCR
0.757
1.362
Table 4 Results of the Discriminant validity – Heterotrait-Monotrait (HTMT)
Dimension
FCF
GR
IFC
OA
SCC
SCR
UR
FCF
GR
0.569
IFC
0.680
0.508
OA
0.626
0.539
0.525
SCC
0.599
0.604
0.483
0.521
SCR
0.574
0.342
0.482
0.625
0.407
UR
0.219
0.214
0.200
0.262
0.113
0.212
Note: FCF: focus on core functionality; GR: Government role; IFC: Inter-firm collaboration; OA: organizational ambidexterity; SCC:
sustainable co-creation; .SCR: substantial cost reduction; UR: University and research organization
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115
4.3. Structural model
The structural model, as suggested by Sang et al. (119), indicates a relationship of cause and effect between the
constructs. It is evaluated to ascertain the predictive power based on the computation of path coefficients and the value
of the R-squared (R2) (120,121). To ascertain the direct correlation between constructs and their degree of significance,
a 10,000 subsample BCa bootstrapping approach was employed (122). R2 was utilized to assess how well the model
accounted for differences in the data. The findings shown in Table 5 indicate that, with an R2 value of 87.6%, the
exogenous variables (IFC, UR, GR, and OA) in the model explains a significant amount of the variance in FIC. Therefore,
there is a significant degree of association between the exogenous and endogenous variables, as shown by the FIC R2
value, which is more than the 26% threshold (123). Likewise, we evaluated the model's predictive power using
PLSpredict. Table 5 displays the findings for Q2 values that are greater than zero, which validates the predictive ability
of the model (122). Table 6 displays the results of the analysis of the associations between the constructs, which include
the beta coefficient (β), p-values, t-values, and f2. The significance of the model was initially assessed by analyzing t-
values and p-values, with Hair et al. (118) specifying that t-values should be greater than or equal to 1.96. In the
aforementioned results (refer Table 6), all t-values were greater than 1.96, with the exception of one specific case - the
direct relationship between UR and FIC. Regarding the p-values, the same constraint holds true: every p-value is less
than 0.05, with one notable exception of the direct relationship between UR and FIC (which does not support hypothesis
H1b). Consequently, hypotheses H1a and H1c are supported. Table 6 also includes the f² effects, which were assessed
in accordance with Cohen's (123) recommendations. Cohen's definition categorizes a large f² impact as being equal to
or greater than 0.35, a medium effect as being at least 0.15, and a minor effect as being at least 0.02. Therefore, with the
exception of the UR–FIC relation, which had a minor impact, it is therefore reasonable to confirm that the findings of the
direct relations test ranged from larger to medium effects. Additionally, it can be concluded that the higher-order
construct of FIC (as shown in Table 6) demonstrates validity across its first-order variables: Focus on Core
Functionalities (β = 0.827), Sustainable Co-Creation (β = 0.765), and Substantial Cost Reduction (β = 0.810).
Table 5 Results of Coefficient of determination (R2) and Stone—Geisser criterion (Q2)
Construct
R-Square
Q-Square
R-Square Adjusted
Frugal innovation capability
0.876
0.607
0.875
Table 6 Direct relationship and test of hypotheses results
Hypothesis
Relationship
Beta Coefficients (β)
T statistics
(t - value)
p-values
f2
Results
H1a
IFC → FIC
0.298
6.996
0.000
0.640
Significant
H1b
UR → FIC
-0.007
0.306
0.760
0.001
Not significant
H1c
GR → FIC
0.214
5.069
0.000
0.309
Significant
Second Order Construct (Frugal innovation)
FIC → FCF
0.827
45.845
0.000
2.156
FIC → SCC
0.765
36.006
0.000
1.410
FIC → SCR
0.810
30.773
0.000
1.914
Note: Significant level (p) ≤ 0.05; → stands for direction of the path; second-order construct reflects the standardized factor loadings of the
indicators on their corresponding latent variables.
4.4. Moderation testing
In this study, we calculated the simple effects at both low and high levels of organizational ambidexterity (OA) using the
outcome of PLS-SEM analysis as shown in Table 7. The findings suggest that OA significantly affects FIC (β = 0.267, t =
3.158, p < 0.05), and that the OA*IFC interaction also has a substantial impact on FIC (β = -0.142, t = 3.536, p < 0.05).
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
116
Thus, hypothesis H2a is supported. These results suggest that OA negatively moderates the relationship between IFC
and FIC, with a higher level of OA weakening this relationship. Additionally, Figure 2 illustrates that the influence of IFC
on FIC is notably stronger at low levels of OA compared to high levels. In addition, the f2 effect size of the moderating
effect is 0.236, which falls within the broad guidelines suggested by Cohen (123). Specifically, effect sizes represent a
minor, medium, and large effect, respectively, at 0.02, 0.15, and 0.35. Hence, the f2 effect size of 0.236 suggests a
moderate moderating impact of OA on the link between IFC and FIC. Moreover, the moderating influence of OA between
UR and FIC was also examined. The findings provide evidence for the substantial impact of OA on FIC (β = 0.267, t =
3.158, P = <0.05). However, FIC is not significantly influenced by the interaction between OA and UR (β = -0.014, t =
0.692, p = 0.489). Therefore, hypothesis H2b is not supported. This is further corroborated by the lack of a significant
direct link between UR and FIC (β = -0.007, t = 0.306, p = 0.760). Consequently, OA does not moderate the relationship
between UR and FIC, since the direct relationship between these variables is not significant. Finally, we investigated the
moderating influence of OA on the association between GR and FIC. The findings provide evidence for significant impact
of OA on FIC (β = 0.267, t = 3.158, P = <0.05), and that the OA*GR interaction also has a substantial impact on FIC (β =
0.067, t = 2.711, P = 0.047). Thus, hypothesis H2c is supported. These results suggest that OA positively moderates the
relationship between GR and FIC, with a higher level of OA strengthening this relationship. Additionally, Figure 3
illustrates that the influence of GR on FIC is notably stronger at high levels of OA compared to low levels. In addition,
the f2 effect size of the moderating effect is 0.072, which falls within the broad guidelines suggested by Cohen (123).
Specifically, effect sizes represent a minor, medium, and large effect, respectively, at 0.02, 0.15, and 0.35. Hence, the f2
effect size of 0.072 suggests a small moderating impact of OA on the link between GR and FIC.
Table 7 Results of moderating effect
Hypothesis
Relationship
Beta Coefficients (β)
T statistics
(t - value)
p-values
f2
Results
H2a
OA x IFC → FIC
-0.142
3.536
0.000
0.236
Significant
H2b
OA x UR → FIC
-0.014
0.692
0.760
0.489
Not significant
H2c
OA x GR → FIC
0.067
2.711
0.047
0.072
Significant
OA → FIC
0.267
3.158
0.002
Figure 2 Moderating effect of OA on IFC and FIC
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Figure 3 Moderating effect of OA on GR and FIC
5. Discussion
This study, in line with the RBV standpoint, investigated and extended the RBV theory by providing a justification for
the significant role of Business network (namely IFC, UR, and GR) in influencing FIC for SMEs operating in EMDEs.
According to this study's empirical results, two of the three hypotheses on the direct relationship were confirmed. In
H1a, the study examines the influence of Inter-firm collaboration (IFC) on FIC, with the results confirming that IFC has
a significant influence on FIC. These results are consistent with those of earlier research that show IFC to be the main
factor influencing businesses' innovation (62,63), and they support Haffar et al. (124) notion that Inter-firm
collaboration allows organizations to share resources and knowledge, which is crucial for frugal innovation. By
collaborating with other firms, organizations can gain insights into market needs and customer preferences, which can
inform their frugal innovation efforts (124). Moreover, the results of this study provide further evidence for the claims
made by Barzotto et al. (125) that collaborating with external partners enables companies, especially SMEs with limited
resources, to tap into a broader spectrum of technological opportunities by sharing knowledge and pooling resources.
This ultimately leads to an improvement in their ability to innovate.
H1b posits that UR has a significant influence on FIC; however, the study's findings do not confirm this, resulting in H1b
not being supported. Surprisingly these findings do not align with previous studies that found that building networks
and collaborations with universities and research institutions (UR) can significantly influence the innovativeness of
SMEs through providing SMEs with access to new knowledge, technologies, and innovative practices that they may not
have developed independently (126). Moreover, the findings did not support the notion that partnerships with UR
significantly bolster both individual and joint innovation capabilities of SMEs (127). However, insignificant influence of
UR on FIC may be due to the fact that UR often focus on basic research, which may not align with the immediate, practical
needs of SMEs. This is especially relevant in the case of EMDEs, where there is a considerable gap between academic
research and industry needs (48,128). There is evidence that weak linkages between academia and industry in many
developing countries, including Tanzania, reduce the potential impact of academic research on SMEs (129). H1c
proposed that GR has a positive influence on FIC, and the results of this study confirm this hypothesis by showing that
GR has a significant and positive impact on FIC, thereby supporting H1c. Government support and institutional policies
are shown to positively affect the innovativeness of SMEs (126). This suggests that when governments offer resources,
funding, or favorable policies, SMEs are more inclined to engage in innovative activities. These align with the results of
Feranita et al. (130) and Choi et al (131), which demonstrated that government funding directly enhances SMEs'
innovation capabilities.
Furthermore, the study investigated the influence of OA as a moderator in the link between the three dimensions of
business networks (IFC, UR, and GR) and FIC. Hypothesis H2a proposed that OA moderates the relationship between
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
118
IFC and FIC. The results indicate that OA negatively moderates this relationship, weakening the positive impact of IFC
on FIC at high levels of OA. This suggests that, although OA is generally beneficial, it may introduce complexities in inter-
firm collaborations that could reduce their effectiveness in promoting frugal innovation. Hypothesis H2b proposed that
OA moderates the relationship between UR and FIC. The study found no significant moderating effect of OA on this
relationship. This implies that collaborations with universities and research organizations may not directly enhance
frugal innovation capability, possibly due to a misalignment between academic research goals and the practical needs
of firms engaged in frugal innovation. The lack of a moderating effect of OA suggests that balancing exploration and
exploitation does not influence this particular type of collaboration in the context of FIC. Lastly, Hypothesis H2c
proposed that OA moderates the relationship between GR and FIC. The study supports this hypothesis, finding that OA
positively moderates this relationship, with the impact of GR on FIC being stronger at higher levels of OA. This finding
implies that government support, when combined with high organizational ambidexterity, can significantly enhance a
small or medium-sized enterprise’s frugal innovation capability.
6. Conclusion
With RBV and DCT as its pillars, this study's main goal was to investigate how business network (IFC, UR and GR)
Influence SMEs' frugal innovation capability. More precise, the goal was to provide a comprehensive analysis of the
multifaceted influence of business network dimensions—IFC, UR, and GR—on SMEs' frugal innovation capability, with
organizational ambidexterity acting as a moderator. The study developed a conceptual model which was then validated
through empirical testing within the Tanzanian manufacturing (SMEs sector. The absence of empirically supported
research on frugal innovation in developing countries, particularly in African contexts, and the inadequate
understanding of the impact of business networks on SMEs' innovation capabilities served as impetuses for this study.
The study’s findings validate that both IFC and GR have a substantial influence on the FIC of SMEs, while UR showed a
negative impact on FIC. Moreover, OA has a moderating role in the link between the dimensions of IFC and GR with FIC,
but UR showed no significant relationship. This highlights that utilizing aggregate business network dimensions as a
unified concept may yield imprecise outcomes in some situations.
6.1. Theoretical implications
The theoretical implications of this study primarily extend and reinforce the RBV and DCT within the context of SMEs
operating in EMDEs. First, the findings build on the RBV by highlighting the critical role of business networks—
specifically IFC and GR—as a strategic resource that significantly influences FIC in SMEs. By confirming that IFC and GR
positively impacts FIC, the study provides empirical support for the RBV's emphasis on the importance of valuable, rare,
inimitable, and non-substitutable (VRIN) resources. It demonstrates that business networks can serve as crucial
external resources that SMEs can leverage to overcome resource constraints and enhance their competitive advantage
through innovation. Second, this study further contributes to the DCT by highlighting the role of dynamic capabilities,
such as organizational ambidexterity, in moderating the association between business networks and frugal innovation.
It suggests that the ability of SMEs to continuously adapt, learn, and reconfigure their resources in response to changing
environments is vital for maximizing the benefits derived from business networks. The finding that OA positively
moderates the link between IFC and FIC reinforces the importance of balancing exploration and exploitation in fostering
innovation, particularly in resource-constrained environments.
Third, interestingly, the study found that collaboration with universities and research organizations (UR) did not
significantly influence FIC, contrary to existing literature. This challenges the assumption that all forms of external
collaboration equally contribute to innovation capabilities in SMEs particularly in developing countries such as
Tanzania. It suggests that the effectiveness of such collaborations may depend on specific contextual factors within
EMDEs, such as the alignment of academic research with market needs, the absorptive capacity of SMEs, or the nature
of the knowledge transferred. This finding calls for a refined comprehension of the ways in which diverse kinds of
business networks foster innovation in various contexts. Fourth, by offering empirical insights into SMEs in the context
of developing and emerging African nations, this study adds to the body of literature by examining the effect of business
networks (IFC and GR) on FIC of manufacturing SMEs in EMDEs, such as Tanzania. Firth, the study highlights that for
SMEs in EMDEs, inter-firm collaboration is particularly crucial in driving frugal innovation. This suggests that, in
resource-constrained environments, SMEs may benefit more from collaborations that directly align with market
dynamics and immediate business needs rather than from academic partnerships. The study emphasizes the need for
SMEs to strategically select and manage their business networks to enhance their innovation capabilities effectively.
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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6.2. Managerial implications
The study presents several significant managerial implications, especially for managers and owners of SMEs operating
in EMDEs, as well as for policymakers. The findings affirm that IFC and GR are all significant in influencing FIC.
Therefore, owners/managers should prioritize developing strong collaborative relationships with other firms. This
could be accomplished through forming alliances, joint ventures, or simply informal partnerships where knowledge and
resources are shared. Regular engagement with partners to understand market needs and technological advancements
can also enhance the firm's ability to innovate frugally. This is particularly crucial in resource-constrained environments
where pooling resources can lead to significant competitive advantages. Moreover, owners/managers should stay
informed about government initiatives and policies that could benefit their innovation activities. This includes applying
for grants, participating in public-private partnerships, and taking advantage of any training or infrastructure
development programs offered by the government. Additionally, managers should consider advocating for more
supportive policies by engaging with industry groups or directly with government representatives. Additionally, the
study found that collaboration with universities and research institutions (UR) did not significantly impact frugal
innovation capability. This suggests that, while such collaborations are generally valued, they might not always translate
directly into practical, frugal innovations for SMEs in EMDEs. Owners/managers should therefore reevaluate the
specific benefits they expect from academic partnerships and ensure that these collaborations are aligned with their
innovation goals. They should focus on partnerships that offer tangible, practical benefits, such as applied research that
directly addresses the firm's innovation challenges, rather than purely theoretical knowledge. Alternatively, they might
consider redirecting resources to more impactful areas of collaboration, such as direct partnerships with other firms.
Furthermore, the notion of organizational ambidexterity, which entails balancing exploitation (focused on efficiency)
and exploration (focused on innovation), is crucial for sustaining competitive advantage. Owners/managers should
ensure that their firms are not overly focused on either exploring new opportunities or exploiting existing resources
but instead maintain a balance that allows for continuous innovation and efficiency. To achieve this, they should develop
strategies that support both the exploration of new ideas and the efficient use of current resources. This could involve
setting up dedicated teams for innovation that work alongside teams focused on improving existing processes. Regularly
reviewing the balance between these two activities will help ensure that the firm remains adaptable and competitive.
Similarly, owners/managers should adopt a context-specific approach to innovation, taking into account the local
market conditions, resource constraints, and the specific needs of their customers. This might involve tailoring
innovation strategies to focus on affordability, usability, and sustainability, all key elements of frugal innovation. In
addition, the study highlights the importance of government support in boosting the frugal innovation capabilities of
SMEs. Policymakers should continue to develop and implement policies that provide financial support, innovation
incentives, and favorable business conditions for SMEs. The weak link between academic research and industry needs,
particularly in developing countries, is a barrier to innovation. Policymakers should foster stronger connections
between universities and SMEs, perhaps by aligning research funding and academic incentives with the practical needs
of the business sector. Moreover, given the positive impact of IFC on innovation, policies that encourage and facilitate
inter-firm collaborations should be prioritized. This could include support for industry clusters, networking events, and
collaborative innovation platforms.
6.3. Limitations and future research directions
The limitations of this study should be taken into consideration when evaluating the results, as they may present
opportunities for future researches. First of all, this study was undertaken with a selected sub-sector of manufacturing
SMEs in Tanzania, which may impact the broader applicability of the findings. Different outcomes may arise when
considering SMEs and large firms across several countries of EMDEs. Hence, it is imperative for future research to
reproduce this study in other EMDEs, especially in Asian and Latin American nations, in order to assess the
generalizability of the study's results. In addition, because of the surprisingly detrimental impact of UR, it is
recommended that future research incorporate a qualitative paradigm into the analysis of the study's data to further
investigate the causes behind this unfavorable outcome. Furthermore, since there is a debate about how the business
network construct is measured and how it affects the findings on business network-innovation, we suggest that future
studies explore other important structural characteristics of business networks to determine if they lead to different
conclusions. This will help to scientifically validate the claim and expand our current understanding. Additionally, cross-
sectional methodology was utilized in this study; nevertheless, it is proposed that future studies evaluate how the
variables used in this study change over time using longitudinal designs, which may provide alternative results and
advance the body of knowledge. In conclusion, control factors like business size and firm category should be
investigated in further research.
World Journal of Advanced Research and Reviews, 2024, 23(03), 104–125
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Compliance with ethical standards
Disclosure of Conflict of interest
No conflict of interest to be disclosed.
Statement of informed consent
Informed consent was obtained from all individual participants included in the study.
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