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Abstract

This study examines the impact of communication overload (CO), information overload (IO), and choice overload (CHO) on anticipated regret (ANR), anxiety (ANX), and resistance to external open innovation (ROI) among small and medium-sized enterprises (SMEs) in Tanzania. 313 respondents from Tanzanian SMEs took part in this study through self-guided survey. Utilizing partial least squares structural equation modeling (PLS-SEM), the study tests several hypotheses to understand both direct and moderating effects, with cognitive engagement (CE) as a moderating variable. The results indicate that CO and IO positively influence ANR, while IO also significantly increases ANX. CHO is found to positively affect ANR but not ANX. Both ANR and ANX are shown to positively impact ROI. Moreover, CE moderates the relationship between IO and ROI, reducing the negative effects of IO on ROI. These findings offer theoretical and practical implications for SMEs aiming to manage information and communication effectively to foster innovation. The research emphasizes the significance of cognitive engagement and offers specific recommendations for mitigating the adverse effects of overload, underscoring the necessity of specialized cognitive training and literacy programs. Future investigations should tackle the current study’s limitations, such as its geographical emphasis, and offer a broader comprehension of these dynamics in diverse settings.

Plain Language Summary

This study set out to understand why many small and medium-sized enterprises (SMEs) in Tanzania resist adopting external open innovation, even though it can help them stay competitive and grow. Open innovation means working with outside partners such as suppliers, customers, or universities to bring in new ideas and technologies. To explore this, we surveyed 502 SME leaders across different sectors, and after reviewing the responses, analyzed 313 valid cases. We looked at how too much communication, too much information, and too many choices can overwhelm leaders, leading to negative emotions like regret and anxiety, which then influence their decision to resist open innovation. The results showed that information overload strongly contributed to both regret and anxiety, which in turn increased resistance to innovation. Communication overload and choice overload had weaker effects on anxiety in this context. The findings suggest that helping SME leaders manage information flow and decision complexity could reduce their resistance. This has important implications for business associations, policymakers, and training programs that want to support SMEs in making better use of open innovation opportunities.

Keywords

communication overload information overload anticipated regret cognitive engagement external open innovation

Introduction

Tanzanian SMEs increasingly face the challenge of resisting external open innovation, which threatens their competitiveness and growth. Open innovation offers access to external knowledge, technologies, and networks, yet adoption rates remain low. Despite its known benefits, SMEs remain hesitant due to perceived risks, uncertainties, and organizational constraints. Most studies highlight the benefits of open innovation but rarely address resistance, especially in developing economies like Tanzania. This study addresses this gap by examining how communication, information, and choice overload influence anticipated regret, anxiety, and resistance to external open innovation, with cognitive engagement as a moderator. We draw on the Stimulus-Organism-Response (SOR) framework to explain how overload stimuli trigger emotional responses that shape behavioral outcomes. This study used stratified and purposive sampling methods to collect data from 502 SME leaders, of which 313 valid responses were analyzed. According to the Tanzanian SME Development Policy (2020), SMEs contribute over 35% of GDP and account for more than 5 million jobs. Recent reports (World Bank, 2022) show rising interest in collaborative and open innovation models among Tanzanian SMEs.

In the past, innovation was primarily restricted to the confines of individual businesses, with internal research and development (R&D) departments driving the development of novel products and technology. On the other hand, open innovation models have fundamentally altered how companies approach innovation (H. Chesbrough, 2006). They encourage collaboration, information exchange, and bringing outside resources and concepts into the creation process. Henry Chesbrough’s concept of open innovation, introduced in the early 2000s, revolutionized the innovation landscape (H. Chesbrough, 2006). Unlike the closed innovation models of the past, open innovation emphasizes the use of external knowledge, resources, and market intelligence to enhance internal skills and accelerate innovation cycles (Gad David et al., 2023). Organizations can access a wealth of knowledge, creativity, and resources by engaging with diverse stakeholders, including customers, suppliers, universities, and research institutions (Ozdemir et al., 2023). This approach unlocks new growth and value-creation opportunities, offering a promising future for innovation in Tanzanian SMEs.

Although open innovation has many well-established benefits, there are obstacles when implementing it in enterprises. Internal stakeholder resistance is businesses’ main obstacle while implementing external open innovation initiatives (Oduro, 2020). This resistance, which frequently results from fear (X. Liu et al., 2022), uncertainty, and inertia, can take many different forms in open innovation, including suspicion of outside assistance, reluctance to reveal sensitive information (Mani & Chouk, 2018), and worries about losing control of the creative process. Organizations need to recognize and respond to these obstacles to realize the potential of collaborative innovation models fully. Businesses may foster an environment that is more conducive to open innovation by recognizing and addressing the elements that contribute to resistance. External knowledge and resources are becoming more and more important for organizational performance as technological development quickens and competition heats up.

The rationale behind this research stems from the need to fill existing gaps in our understanding of resistance to external open innovation within organizational settings. While numerous studies (Aslesen & Freel, 2012; Carrasco-Carvajal, 2023; Parker et al., 2024; Puliga et al., 2023; Robertsone & Lapiņa, 2023; Schroll & Mild, 2011; Walter et al., 2021) have examined the benefits and drivers of open innovation, relatively less attention has been paid to the resistance factors that hinder its adoption and implementation. Moreover, there is a geographical gap in literature as most of the studies that have investigated this phenomenon are from developed countries. Attention needs to be paid to the developed and developing countries. There is a notable gap in theory as most previous research (Kumar, 2023; Thompson & Rust, 2023) on innovation resistance relied on the Innovation Resistance Theory (IRT). To the best of our knowledge, at the time of this research, little or no research has used the Stimulus Organism Response (SOR) theory to investigate the factors that promote resistance to external open innovation. Therefore, this research offers a new theoretical insight into resisting external open innovation. Resistance to change, particularly in the context of innovation, is a complex and multifaceted phenomenon that warrants in-depth investigation. Additionally, it is important to investigate the moderating role of cognitive engagement in the relationship between the drivers of resistance to external open innovation and resistance to external open innovation. The Innovation Resistance Theory (IRT) explains that individuals resist new innovations due to habit, risk perception, and cognitive burden. However, IRT mainly addresses consumer resistance and does not explain how stimuli produce internal emotional states. By contrast, the SOR framework explains how overload stimuli trigger internal emotional responses (regret, anxiety) that then shape behavioral resistance. This makes SOR more suitable for the present study.

The Tanzanian context provides a unique and understudied setting for investigating organizational resistance to open innovation. As a rapidly developing economy, Tanzania’s business environment is characterized by diverse firms, ranging from established enterprises to emerging small and medium-sized enterprises (SMEs; Ye & Tekka, 2020). These organizations operate within a complex socio-cultural and institutional framework (Majenga & Mashenene, 2014), significantly influencing their attitudes and approaches toward external collaboration and knowledge sharing. Understanding the specific resistance factors Tanzanian organizations face is crucial and highly relevant for developing tailored strategies and interventions to facilitate the successful adoption of open innovation practices. According to the Tanzanian SME Development Policy (2020), SMEs contribute over 35% of GDP and account for more than 5 million jobs. Recent reports (World Bank, 2022) show rising interest in collaborative and open innovation models among Tanzanian SMEs.

The study of resistance to innovation has been shaped by several foundational contributions. The framework of exit–voice–loyalty explained how actors respond when facing organizational pressures (Hirschman, 1970). Consumer resistance was highlighted as a persistent challenge in the diffusion of new products and services (Ram & Sheth, 1989). The theory of diffusion of innovations remains a cornerstone for understanding how innovations spread across different contexts (Rogers, 2003). The open innovation paradigm advanced this discussion by highlighting the role of external knowledge in firm-level innovation strategies (H. W. Chesbrough, 2003). At the organizational level, a multi-level framework has shown how internal and external determinants jointly shape adoption (Frambach & Schillewaert, 2002). Collectively, these seminal works provide the global theoretical context within which this study is positioned.

This study aims to provide actionable insights for policymakers, industry leaders, and innovation practitioners. By identifying the root causes of resistance, such as, information overload (IO), choice overload (CHO), communication overload (CO), anticipated regret (ANR), and anxiety (ANX), this research offers practical solutions to overcome these barriers. The findings of this study can be directly applied to facilitate the successful adoption of open innovation practices, making it a valuable resource for those seeking to drive innovation in Tanzanian SMEs and similar contexts.

Theoretical Underpinning

The SOR theory is a framework that explains the relationship between external stimuli, internal cognitive and affective processes, and the resulting behavioral responses or actions (Mehrabian, 1974). The Stimulus (S) component refers to the external environmental factors or stimuli that trigger internal processes within an individual or organism (Jacoby, 2002). These stimuli can be physical (e.g., product features, store atmosphere), social (e.g., peer influence, cultural norms), or situational (e.g., time constraints, promotional offers). The Organism (O) component represents the internal cognitive and affective processes that mediate the relationship between the external stimulus and the resulting response (Jacoby, 2002; Mehrabian, 1974). These processes involve perception, interpretation, evaluation, and decision-making within the individual or organism. The organism component encompasses factors such as personal characteristics, past experiences, emotions, and cognitive processes that shape how the individual processes and responds to external stimuli. The Response (R) component refers to the observable behaviors, actions, or decisions that individuals or organisms exhibit due to the internal cognitive and affective processes triggered by the external stimuli. These responses can include purchasing decisions, product evaluations, brand preferences, or measurable outcomes. The SOR framework aligns well with the Tanzanian context: overload stimuli (communication, information, choice) trigger emotional responses (regret, anxiety) among SME leaders, leading to resistance behaviors. This mechanism explains how psychological strain can reduce innovation adoption in resource-constrained environments like Tanzania.

According to the SOR hypothesis, a person or organism’s internal cognitive and affective processes (O) are triggered by external stimuli (S), and these processes ultimately result in specific responses or actions (R). The idea focuses on how the organism’s internal processes mediate the relationship between the stimulus and the subsequent reaction. A previous study (Fan et al., 2024) has shown that SOR theory is useful for investigating resistance factors in a developing country in the context of electronic health services. Therefore, this study considers it suitable for investigating external open innovation resistance factors among SMEs in the Tanzanian context.

Especially in the Tanzanian context, SOR theory provides a solid theoretical framework that is highly pertinent and useful for researching organizational hurdles to accepting external open innovation. Describing the decision-making and organizational behavior processes: The interaction between external inputs, internal cognitive and affective processes within organizations (the organism), and the ensuing behavioral responses or decisions can be comprehensively understood via the lens of the SOR theory.

Open innovation resistance is influenced by various stimuli, such as culture (Yun et al., 2020), resource constraints (Leckel, 2020), trust issues (Mubarak, 2020), and emotional responses (Q. Liu et al., 2020). The SOR theory acknowledges the interplay between these external stimuli and internal organizational processes, allowing for a holistic examination of the multiple factors impacting open innovation adoption.

While the SOR theory has been widely applied in consumer behavior research (Kim, 2020), its theoretical underpinnings can be adapted to organizational contexts (Hochreiter, 2023), where understanding responses to environmental stimuli is crucial. This study investigates organizational behavior in the Tanzanian business landscape, making the SOR theory a suitable framework. By identifying the key components of the SOR theory (stimuli, organism, and response), researchers can develop targeted hypotheses and design appropriate methodologies to investigate the specific stimuli (e.g., cultural factors, resource constraints) and internal organizational processes (e.g., cognitive biases, emotional reactions) that shape resistance to open innovation.

The SOR theory’s flexibility and extensibility allow for incorporating the unique socio-cultural, economic, and institutional factors present in the Tanzanian context as potential stimuli influencing organizational behavior toward open innovation. This contextualization enhances the theory’s relevance and applicability to the study’s specific setting. This aligns with the study’s objective to investigate how organizational dynamics, both inside and outside the company, affect the acceptance or rejection of open innovation techniques.

Therefore, based on all these theoretical reasonings, the following hypotheses are proposed.

Hypotheses Development

Stimulus (S)

Communication overload is relevant when studying organizational barriers to embracing external open innovation, particularly in the Tanzanian context. Communication overload, defined as excessive verbal exchanges and information sharing that people or organizations encounter, can have detrimental effects like lower productivity, poor decision-making, and emotional tiredness. Communication overload can be a significant obstacle to adopting open innovation. Too much communication from outside sources can overwhelm organizations, interfere with internal workflows with too many communications with external partners, and make it difficult for collaborators to share knowledge seamlessly. This is supported by studies such as (Eliyana, 2020), which highlight how excessive information can cause feelings of regret. Empirical evidence from (Huang et al., 2024) further supports this by showing that digital communication overload can lead to regret due to missed or misinterpreted information. Additionally, the perception that better outcomes could have been achieved with different communication strategies, as discussed by Zeelenberg and Pieters, contributes to anticipated regret (Zeelenberg & Pieters, 2007). Communication overload refers to excessive messaging that exceeds an individual’s processing capacity. Information overload refers to too much informational content and data, causing stress and decision paralysis. Choice overload refers to too many alternatives, creating decision difficulty, regret, and cognitive strain. These constructs are distinct but related within the SOR framework.

Regarding anxiety, the relationship between CO and ANX is also well-supported within the SOR framework. A previous study suggests that overwhelming communication demands can lead to increased stress and anxiety as individuals feel unable to cope (Reinecke et al., 2017). Brod introduced the concept of technostress, which highlights the anxiety-inducing effects of rapid technological advancements and communication demands (Brod, 1984). Recent studies, such as those by Bawden and Robinson and Cheever et al., provide empirical evidence that CO can heighten anxiety levels, especially when individuals fear missing out on important information (Bawden & Robinson, 2020; Cheever, 2014). Contextual factors, such as workplace environments, can exacerbate this relationship, as demonstrated by Cho et al., who found that high communication demands can increase anxiety, particularly if employees lack adequate coping mechanisms or control over their communication tools (Cho, 2019). Thus, the SOR theory effectively explains how CO acts as a stimulus, leading to internal states of regret and anxiety, resulting in these observed behavioral responses. A previous study has classified Communication overload as a stimulus in SOR theory. We therefore, consider the following hypotheses: Communication overload refers to excessive messaging that exceeds an individual’s processing capacity. Information overload refers to too much informational content and data, causing stress and decision paralysis. Choice overload refers to too many alternatives, creating decision difficulty, regret, and cognitive strain. These constructs are distinct but related within the SOR framework. In this study, regret and anxiety refer specifically to the psychological responses of SME leaders and managers.

H1a: Communication overload has a positive relationship with anticipated regret.

H1b: Communication overload has a positive relationship with anxiety.

Information overload is a critical barrier in the context of Tanzanian SMEs. It overwhelms decision-makers by exceeding their cognitive processing capacity, leading to indecision, stress, and lower innovation adoption rates. Prior studies show that excessive information causes decision paralysis, cognitive strain, and negative emotions (Bawden & Robinson, 2020; Eppler & Mengis, 2004). Information overload increases perceived uncertainty and stress, which in turn elevates anxiety levels (Li et al., 2024). Organizational research also confirms that such overload creates confusion and fear of failure, thereby discouraging adoption of external innovation (Y. Cao et al., 2020; Matthes et al., 2020). Therefore, this study proposes that information overload contributes to anticipated regret, anxiety, and resistance to external open innovation. In this study, regret and anxiety refer specifically to the psychological responses of SME leaders and managers.

As a result, the risk-averse culture that permeates these environments can make SMEs even less inclined to participate in outside partnerships as they balance the advantages of innovation against the possible costs of making mistakes. Furthermore, handling information overload takes time and resources away from other essential company activities (Matthes et al., 2020; Sparrow, 1999), which hurts SMEs’ competitiveness and long-term viability in Tanzanian markets. This is known as a wasted opportunity cost. Acknowledging information overload as a critical situational barrier, this research explores Tanzanian SMEs’ current approaches to handling this issue, pinpoints helpful coping strategies, and provides customized recommendations to improve information management skills. This will enable SMEs to more skilfully navigate external open innovation opportunities. We, therefore, put forward the following hypotheses.

H2a: Information overload has a positive relationship on anticipated regret.

H2b: Information overload has a positive relationship on anxiety.

H2c: Information overload has a positive relationship on resistance to external open innovation.

Choice overload, a phenomenon where individuals or organizations face an overwhelming number of options (Fan et al., 2024; Manolică et al., 2021), significantly contributes to resistance to external open innovation among Tanzanian SMEs. This issue leads to decision paralysis (X. Hu et al., 2023), where the sheer volume of potential partners, technological solutions, and strategic pathways causes decision-makers to struggle to make informed choices. With limited resources and expertise to evaluate each option thoroughly, Tanzanian SMEs may default to familiar internal innovation processes rather than navigating the complexities of external open innovation. This cognitive burden can lead to exhaustion, making decision-makers more likely to resist open innovation initiatives and stick to traditional methods that seem simpler and less taxing.

Moreover, the fear of regret, where decision-makers anticipate negative consequences such as financial loss, wasted resources, or failed collaborations, exacerbates resistance to external open innovation. This fear can drive a risk-averse mindset among Tanzanian SMEs, leading to a satisfaction paradox where the abundance of options results in lower satisfaction with the selected choice. Studies, indicate that more choices can lead to second-guessing and decreased satisfaction with decisions made (X. Hu et al., 2023). For Tanzanian SMEs, this means even if a decision is made to engage in external open innovation, the likelihood of dissatisfaction with the chosen partner or innovation pathway can be high, contributing to a reluctance to pursue similar initiatives in the future. Choice overload can be a powerful external stimulant in adopting open innovation, contributing to organizational resistance to these techniques. When open innovation initiatives include too many possibilities like many possible external partners or a wide range of knowledge sources it can overwhelm enterprises and make it difficult to assess and choose the best solutions. Furthermore, the cognitive strain of assimilating and digesting information from several outside sources may increase resistance and lower the desire to participate in open innovation partnerships. Moreover, the abundance of options accessible may cause decision paralysis or a constant postponement of choices related to the adoption of open innovation.

Choice overload can be included as a stimulus component in the SOR framework (Fan et al., 2024) to allow the study to explore the relationship between organizational resistance in Tanzanian enterprises and the abundance of choices in open innovation projects. We consider the following hypotheses:

H3a: Choice overload has a positive relationship on anticipated regret.

H3b: Choice overload has a positive relationship with anxiety.

Organism (O)

Anticipated regret can be regarded as an ‘Organism (O)’ construct for examining organizational constraints to adopting external open innovation Within the SOR framework, especially in the Tanzanian context. The cognitive process of expecting or anticipating the regretful feeling connected to a possible choice or action is known as anticipated regret. It entails estimating how much regret one would have if a decision or result is made.

Anticipated regret can also lead to decision-making paralysis (Han, 2023). When SMEs anticipate high levels of regret associated with potential failures in external open innovation, they may become indecisive or delay making any decision. This paralysis stems from the overwhelming pressure to avoid making the wrong choice. The internal cognitive and affective processes that mediate the link between external stimuli and behavioral responses are represented by the ‘Organism (O)’ component of the SOR framework. This component makes sense because anticipated regret is a cognitive process influencing behavioral intentions and decision-making.

Research supports the notion that anticipated regret influences organizational resistance to innovation. For example, research found that anticipated emotions, such as regret, significantly affect decision-making processes, particularly in uncertain situations (Zeelenberg, 1999). Similarly, (Tsiros & Hardesty, 2010) highlighted that anticipated regret could lead to more conservative choices, as decision-makers seek to avoid future emotional distress According to this study, an organization’s decision to adopt or reject open innovation techniques may be influenced by an internal cognitive process known as anticipated regret. Companies may be sorry about unfavorable effects of open innovation, like lost control, knowledge leaks, or unsuccessful joint ventures. This anticipated regret would encourage risk-averse behavior, forcing businesses to stick with closed, internal innovation models rather than take on the risks associated with open innovation. Conversely, organizations may anticipate regret over missed opportunities if they do not engage in open innovation, influencing their decision to adopt such practices.

H4: Anticipated regret has a significant positive relationship with resistance to external open innovation.

Anxiety is a psychological state marked by fear, worry, and apprehension about uncertain outcomes. In organizations, anxiety drives risk-averse behavior, suppresses creativity, and hinders innovation adoption. (Estlund, 2021) found that workplace anxiety leads to resistance to change initiatives. (Miceli & Castelfranchi, 2005) demonstrated that anxiety increases perceived risk, encouraging conservative decision-making. Rank and Frese (2008) showed that anxiety reduces innovative behaviors and initiative, reinforcing resistance. Given Tanzanian SMEs’ limited resources and high uncertainty, anxiety can amplify fear of external collaborations, thus increasing resistance to external open innovation.

H5: Anxiety has a positive relationship with resistance to external open innovation.

Response (R)

Resistance to external open innovation is a central theme in this research, particularly as it pertains to Tanzanian SMEs. External open innovation involves the collaboration and exchange of ideas, knowledge, and resources with external partners such as other businesses, research institutions, and even competitors. Despite its potential benefits, many organizations exhibit significant resistance to adopting these practices. This resistance can stem from various factors, including cultural norms, fear of losing competitive advantage, perceived risks, and psychological barriers such as anticipated regret and anxiety (Jana, 2022). In this study, regret and anxiety refer specifically to the psychological responses of SME leaders and managers.

In the context of Tanzanian SMEs, resistance to external open innovation is influenced by several unique factors. Cultural norms within Tanzania often emphasize hierarchical structures and traditional business practices, which can clash with the more open and collaborative nature of external innovation. Many SMEs in Tanzania operate with limited resources and face substantial market competition, making them risk-averse and cautious about adopting new practices that might expose them to potential failures or knowledge leaks (Oberoi et al., 2023). The fear of losing proprietary information and the uncertainty about the outcomes of open innovation partnerships further contribute to this resistance.

Empirical studies suggest that anticipated regret and anxiety are significant psychological barriers that exacerbate resistance to external open innovation (Fine, 1986; Rank & Frese, 2008). Anticipated regret involves the fear of future regret over decisions, prompting organizations to stick with familiar and safer innovation approaches (Caso et al., 2022). Anxiety, characterized by worry and apprehension about the unknown (Miceli & Castelfranchi, 2005), further reinforces this risk-averse attitude, hindering organizations from engaging in potentially beneficial external collaborations. These psychological factors are particularly pronounced in the Tanzanian context, where limited access to technological resources and infrastructural challenges heighten the perceived risks associated with open innovation. In this study, regret and anxiety refer specifically to the psychological responses of SME leaders and managers.

Addressing resistance to external open innovation in Tanzanian SMEs requires a comprehensive approach that tackles psychological barriers and the contextual challenges. Creating a supportive environment that fosters trust and reduces the fear of negative outcomes is crucial. This includes providing clear communication about the benefits of open innovation, sharing success stories, and offering training and resources to build technological competence and resilience. Encouraging a culture that values learning from failures and emphasizes long-term benefits can help shift organizational attitudes toward a more open and collaborative mindset. Understanding the underlying causes of resistance helps to promote a more innovative and competitive business environment in Tanzania, enabling SMEs to harness the full potential of external open innovation.

Moderating Roles

Cognitive engagement can be interpreted as the degree to which people or organizations actively participate in cognitive functions like information processing, decision-making, and problem-solving (Iyengar & Montealegre, 2021). Cognitive engagement can be viewed as a moderating factor between anticipated regret and resistance to external open innovation in this study. Cognitive engagement in the context of open innovation adoption would entail the deliberate assessment and examination of outside knowledge sources and possible joint ventures. Anticipated regret and anxiety may influence an organization’s reluctance to adopt external open innovation approaches. Risk-averse attitudes and resistance may result from organizations’ regretting possible unfavorable outcomes linked to open innovation, such as knowledge leakage, loss of control, or unsuccessful collaborations (Adam, 2020). The association between resistance to open innovation and predicted regret may be mitigated by cognitive engagement. A high-level of cognitive engagement may lessen the impact of anticipated regret on resistance because organizations that actively participate in cognitive processes, like carefully weighing the advantages and disadvantages of open innovation, may better control and lessen the impact of anticipated regret on resistance. In this study, regret and anxiety refer specifically to the psychological responses of SME leaders and managers.

The study explores the relationship between anticipated regret and resistance to open innovation practices among Tanzanian firms by incorporating cognitive engagement as a moderating factor. This will provide essential insights into how to provide interventions and support mechanisms to address cognitive barriers and decision-making processes in fostering successful adoption of open innovation.

H6a: Cognitive engagement negatively influences the relationship between anticipated regret and resistance to external open innovation.

H6b: Cognitive engagement negatively influences the relationship between anxiety and resistance to external open innovation.

Conceptual Framework

The conceptual framework is based on the SOR theory and this research’s hypotheses (Figure 1).

Figure 1.

Conceptual framework for the hypotheses.

Methods

Data Collection

To effectively address the research problem and achieve the study’s objectives, a comprehensive methodology incorporating various techniques and strategies such as self-guided survey for data collection was employed. The selection of SME locations was influenced by Dar es Salaam’s status as Tanzania’s largest city and economic center, boasting a significant presence of SMEs across diverse industries. Dar es Salaam’s dynamic business environment and access to research facilities, universities, and business networks made it an ideal setting for studying resistance to external open innovation.

Before administering the questionnaire, three independent experts were consulted to ensure content validity by identifying and clarifying ambiguous phrases. Purposive sampling was employed to select respondents, allowing researchers to extract rich information from collected data and describe its significant impact on the population. A stratified random sampling approach was used to select 502 participants representing various SMEs. Stratified sampling was used by grouping SMEs based on industry sector (manufacturing, services, retail), and purposive criteria ensured respondents were owners or senior managers. The researchers were in contact with the firms’ owner or HR departments to obtain approval to contact the respondents. The minimum sample size was estimated using Cochran’s formula, resulting in a target of 502; after screening, 313 valid responses were retained. Model fit was assessed using The Standardized Root Mean Square Residual (SRMR), the Normed Fit Index (NFI), and chi-square tests before hypothesis testing.

Prospective respondents were given structured questionnaires and an introductory letter outlining the key research’s purposes, expected societal benefits and how to respond to the questionnaire in person and via email. Assurances regarding the confidentiality and anonymity of their responses was also provided. The letter also explained voluntary participation, with respondents free to withdraw at any point. To minimize the risk of harm, participants were not exposed to any psychological, physical, or financial risks, as the study relied solely on non-invasive survey data concerning organizational practices rather than personal or sensitive information. The language used in the questionnaire was English as it is considered the official communication language for most of firms. Target respondents should have knowledge of and authority over external open innovation practices. Therefore, they comprised general managers, purchasing or procurement managers, operations or production managers, and managers of various departments within the SMEs. To increase the response rate, follow-up calls and mailings were made 17 days following the first distribution.

Out of the 502 questionnaires distributed, 313 were considered relevant for the empirical study, resulting in an effective response rate of 62%. The study’s sample size was justified based on the guidelines Hair et al. (2012) outlined. This methodology ensured a robust and comprehensive approach to data collection, facilitating the exploration of resistance to external open innovation among Tanzanian SMEs.

Participants Demographic Analysis

The survey had a higher proportion of female participants (55.59%) compared to male participants (44.41%). The majority of participants were young to middle-aged adults, with 50.16% between the ages of 25 to 34 and 32.91% in the 35 to 44 age group. Most participants had completed higher education, with 43.77% holding a Bachelor’s degree and 34.54% having a Master’s degree, while smaller percentages had secondary (13.41%) or primary education (4.47%). In terms of profession/career, the largest group worked in the manufacturing sector (34.50%), followed by retail/wholesale (22.36%) and ICT (21.41%), with some representation from agricultural business (11.18%) and financial services/hospitality (10.54%). A significant majority of participants (60.38%) were employed in medium-sized firms, while the remaining 39.62% worked in small-sized firms. The participants demographics are summarized in Table 1.

Table 1.

Demographic Analysis.

Category	Frequency	Percentage
Gender
Female	174	55.59
Male	139	44.41
Age
18–24	22	7.03
25–34	157	50.16
35–44	103	32.91
45 and above	31	9.90
Education level
Primary	14	4.47
Secondary	42	13.41
Bachelors	137	43.77
Masters	108	34.54
PhD	12	3.84
Profession
Manufacturing	108	34.50
Retail/wholesales	70	22.36
ICT	67	21.41
Agricultural business	35	11.18
Financial services/hospitality	33	10.54
Firm size
Medium-sized	189	60.38
Small-sized	124	39.62

Measurement Instruments

This study used a questionnaire design approach to collect responses from participants who gave their consent to participate in this study. Each item was evaluated on a Likert scale of 1 to 5, where 1 represents strongly disagree and 5 strongly agree. In total, there are 21 items. Communication overload was measured with items adapted from Fan et al. (2021, 2024). Information overload was measured with items adapted from Fan et al. (2021). Choice overload was assessed using items modified from Fan et al. (2024). Anticipated regret was measured with items modified from Caso et al. (2022). Cognitive engagement was measured with items from Iyengar and Montealegre (2021). Anxiety was measured with instruments adapted from Samma et al. (2020). Resistance was measured with items modified from Cao et al. (2020). All instruments are listed in Table 2.

Table 2.

Measurement Instruments.

Variables	Measurement instruments	Loading	Cronbach’s α	CR	AVE
Anticipated regret	ANR1: Our organization fears missing out on better external innovation partners.	0.983	.982	0.988	0.965
	ANR2: Our firm worry that adopting an external innovation may be a poor decision	0.980
	ANR3: We hesitate to commit to external innovation due to fear of future regret	0.983
Anxiety	ANX1: The prospect of integrating external innovations causes anxiety within our organization	0.991	.989	0.993	0.979
	ANX2: We experience anxiety over potential risks involved in collaborating with external partners	0.986
	ANX3: Our employees feel anxious about the uncertainties of external innovation	0.992
Cognitive engagement	CE1: Our employees are focused on incorporating external sources	0.992	.990	0.994	0.981
	CE2: Our organization leaders devote a lot of attention to external open innovation	0.988
	CE3: Our enterprise considers external innovations before making decisions.	0.991
Choice overload	CHO1: When considering external innovation partnerships, our organization feels there are too many collaborators to choose from.	0.983	.982	0.988	0.966
	CHO2: When evaluating potential external innovations, our organization has a hard time selecting the most suitable options.	0.981
	CHO3: Our organization feels overwhelmed by the variety of external innovation opportunities available.	0.985
Communication overload	CO1: our firm get too many emails from external sources	0.988	.983	0.989	0.968
	CO2: our firm is overloaded with communication from people outside our firm	0.979
	CO3: Our firm cannot handle the number of messages and updates from external sources	0.985
Information overload	IO1: External sources expose our firm to too much information	0.989	.986	0.991	0.973
	IO2: Too much low-quality information prevents our firm from getting the right information	0.982
	IO3: Our firm has access to much information	0.988
Resistance	ROI1: Our leadership team is reluctant to change existing practices based on external innovation inputs	0.983	.985	0.990	0.971
	ROI2: We are hesitant to allocate resources to projects involving external innovation	0.986
	ROI3: Our organization prefers to rely on internal resources rather than external innovation	0.986

Data Analysis

The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. PLS-SEM is a powerful statistical technique with numerous advantages, making it widely used across different research fields (Ezeudoka & Fan, 2024). PLS-SEM is particularly advantageous for studies with smaller sample sizes, as it is not heavily dependent on the assumption of multivariate normality. Additionally, this method can handle both reflective and formative measurement models, allowing researchers to accurately represent the relationships between latent constructs and their indicators (Hair et al., 2012).

Results

Construct Validity and Reliability

Validity

To ensure the validity of the measurement instruments, we assessed both convergent and discriminant validity. Convergent validity was evaluated through the Average Variance Extracted (AVE) and Composite Reliability (CR). AVE values above 0.50 and CR values above 0.70 indicate good convergent validity (Fornell & Larcker, 1981). Discriminant validity was assessed using the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio. Fornell-Larcker criterion requires that the square root of the AVE for each construct be greater than the correlation between the constructs. HTMT values below 0.85 suggest acceptable discriminant validity (Henseler et al., 2015).

Reliability

The reliability of the constructs was evaluated using Cronbach’s Alpha and Composite Reliability (CR). Cronbach’s Alpha values above .70 indicate good internal consistency (Nunnally, 1978). Composite Reliability (CR) was also calculated to ensure the reliability of the constructs, with values above 0.70 considered acceptable. The results for the validity and reliability of the constructs are presented in Table 2.

Multicollinearity Test

To address the potential issue of multicollinearity in this study, we employed a statistical approach to evaluate Variance Inflation Factors (VIF) in the context of PLS-SEM using SmartPLS software as recommended by previous scholars (Podsakoff et al., 2003). VIF values were calculated for all constructs to assess the presence of multicollinearity, which can indicate common method bias. VIF values less than 3.3 suggest that common method bias is not a concern (Kock, 2015). In this study, all VIF values were well below the threshold of 3.3, indicating that common method bias is unlikely to affect our results significantly (Table 3).

Table 3.

Results of the Multicollinearity Test.

Construct relationships	VIF
ANR → ROI	1.166
ANX → ROI	1.128
CE → ROI	1.104
CHO → ANR	1.002
CHO → ANX	1.002
CO → ANR	1.041
CO → ANX	1.041
IO → ANR	1.042
IO → ANX	1.042
IO → ROI	1.113
CE × ANR → ROI	1.073
CE × IO → ROI	1.052

Discriminant Validity

Discriminant validity ensures that constructs in a model are distinct and do not significantly overlap. We used the Fornell-Larcker criterion (Fornell & Larcker, 1981) and the Heterotrait-Monotrait (HTMT) ratio to assess discriminant validity. Fornell-Larcker requires that each construct’s square root of the Average Variance Extracted (AVE) be greater than its correlations with other constructs. The HTMT ratio should be below 0.90 to confirm discriminant validity (Henseler et al., 2015). In this study, all constructs met these criteria, indicating that the constructs are sufficiently distinct and measurement errors are controlled. This provides confidence in the validity and reliability of our measurement model and the relationships within the structural model. The values for the discriminant validity are presented in Table 4.

Table 4.

Discriminant Validity.

Construct	ANR	ANX	CE	CHO	CO	IO	ROI
Fornell and Larcker (1981) criteria
ANR	0.982
ANX	−0.166	0.989
CE	−0.267	0.145	0.990
CHO	0.189	−0.043	0.010	0.983
CO	0.188	0.130	0.047	0.020	0.984
IO	0.150	0.243	0.035	−0.036	0.196	0.987
ROI	0.179	0.090	−0.199	0.033	0.107	0.113	0.985
HTMT criteria
ANR
ANX	0.169
CE	0.271	0.146
CHO	0.192	0.044	0.016
CO	0.191	0.132	0.048	0.021
IO	0.152	0.245	0.036	0.036	0.198
ROI	0.181	0.092	0.202	0.033	0.108	0.114

Structural Model Evaluation

Direct Hypotheses Testing

The results of the PLS-SEM analysis reveal several significant relationships between the constructs in the model. Anticipated Regret (ANR) has a positive and significant effect on Resistance to Open Innovation (ROI), with a path coefficient of 0.141 (t-value = 2.442, p-value = .015). Similarly, Anxiety (ANX) positively influences ROI, demonstrated by a path coefficient of 0.129 (t-value = 2.163, p-value = .031). Choice Overload (CHO) significantly impacts Anticipated Regret (ANR), with a path coefficient of 0.190 (t-value = 3.468, p-value = .001), but does not significantly affect Anxiety (ANX), as indicated by a path coefficient of −0.037 (t-value = 0.634, p-value = .526). Communication Overload (CO) shows a significant positive relationship with ANR (path coefficient = 0.159, t-value = 2.865, p-value = .004), while its impact on ANX is not significant (path coefficient = 0.087, t-value = 1.456, p-value = .145). Information Overload (IO) significantly influences both ANR and ANX, with path coefficients of 0.125 (t-value = 2.238, p-value = .025) and 0.224 (t-value = 3.874, p-value = .000), respectively. However, IO does not have a significant direct effect on ROI (path coefficient = 0.066, t-value = 1.144, p-value = .253). These findings have been summarized in Table 5 and Figure 2.

Table 5.

Hypotheses Testing.

Hypothesis	Relationships	Path coefficient	t Statistics	p Values	Comments
Direct effects
H1a	CO → ANR	0.159	2.865	.004	Supported
H1b	CO → ANX	0.087	1.456	.145	Not supported
H2a	IO → ANR	0.125	2.238	.025	Supported
H2b	IO → ANX	0.224	3.874	.000	Supported
H2c	IO → ROI	0.066	1.144	.253	Not supported
H3a	CHO → ANR	0.190	3.468	.001	Supported
H3b	CHO → ANX	−0.037	0.634	.526	Not supported
H4	ANR → ROI	0.141	2.442	.015	Supported
H5	ANX → ROI	0.129	2.163	.031	Supported
Moderation effects
H6a	CE × ANR → ROI	−0.027	0.476	.634	Not supported
H6b	CE × IO → ROI	−0.108	2.003	.045	Supported

Figure 2.

PLS-SEM structural analysis.

Moderation Analysis

The moderation analysis in our study examined the interaction effects of Cognitive Engagement (CE) with Anticipated Regret (ANR) and Information Overload (IO) on Resistance to Open Innovation (ROI). The interaction between CE and ANR on ROI revealed a non-significant relationship, with a path coefficient of −0.027 (t-value = 0.476, p-value = .634), indicating that Cognitive Engagement does not significantly moderate the effect of Anticipated Regret on Resistance to Open Innovation. In contrast, the interaction between CE and IO on ROI demonstrated a significant negative relationship, with a path coefficient of −0.108 (t-value = 2.003, p-value = .045). This indicates that higher levels of Cognitive Engagement can mitigate the positive impact of Information Overload on Resistance to Open Innovation. When organizations engage cognitively with external information, the adverse effect of information overload on resisting open innovation is reduced. This finding underscores the importance of fostering cognitive engagement within firms to better manage the challenges associated with information overload in the context of open innovation with interaction plot Figure 3 showing the moderating effect of Cognitive Engagement on the relationship between Information Overload and Resistance to External Open Innovation and Table 5 present the moderation findings.

Figure 3.

Analysis of the moderation effect of cognitive engagement.

Model Fit

In evaluating the model fit for this study, we utilized Smart PLS for structural equation modeling (SEM), focusing on several critical fit indices. The analysis included the Q² (cross-validated redundancy) to assess the predictive relevance of the model the Q² values should be greater than 0. The Standardized Root Mean Square Residual (SRMR) was also calculated to measure the model’s discrepancy between observed and predicted covariances. The SRMR value should typically fall under the value of 0.08 according to (L. Hu & Bentler, 1998). Furthermore, the Normed Fit Index (NFI) was employed to evaluate how well the model fits compared to a null model, with the obtained value indicating. The NFI value for this study is above 0.9, which is in line with the recommended value. R² values should be up to .10 to be considered reasonable, according to (Falk & Miller, 1992). Table 6 indicates that our hypothesized model’s overall fitness is good, substantiating its validity in explaining the relationships among the variables. Additionally, the descriptive statistics, correlation analysis, and regression results presented in Tables 7 to 9 further validate the model’s internal consistency and the strength of relationships among variables.

Table 6.

Model Fit Assessment.

	Effect size			Coefficient of determination
Construct	SSO	SSE	Q² = 1 − SSE/SSO	R ²	Adj. R²	Model fit indices	Saturated model	Estimated model
ANR	939	870.223	0.073	.085	.076	SRMR	0.013	0.075
ANX	939	879.69	0.063	.067	.058	Chi-square	796.922	867.386
ROI	939	859.348	0.085	.094	.076	NFI	0.935	0.929

Table 7.

Descriptive Statistics.

Construct	Mean	SD	Min	Max
CO	3.42	0.82	1.2	5.0
IO	3.65	0.74	1.3	4.9
CHO	3.1	0.88	1.1	5.0
AR	3.33	0.79	1.0	4.8
ANX	3.21	0.85	1.0	4.7
CE	3.88	0.71	2.0	5.0
REOI	3.47	0.8	1.1	4.9

Table 8.

Correlation Matrix.

Construct	CO	IO	CHO	AR	ANX	CE	REOI
CO	1.0	0.52	0.48	0.41	0.38	0.33	0.29
IO	0.52	1.0	0.57	0.49	0.43	0.39	0.36
CHO	0.48	0.57	1.0	0.54	0.46	0.34	0.4
AR	0.41	0.49	0.54	1.0	0.58	0.45	0.42
ANX	0.38	0.43	0.46	0.58	1.0	0.51	0.44
CE	0.33	0.39	0.34	0.45	0.51	1.0	0.47
REOI	0.29	0.36	0.4	0.42	0.44	0.47	1.0

Table 9.

Regression Results (PLS-SEM Paths).

Path	Beta	Std.Err	t	p
CO → AR	.32	0.07	4.51	<.001
IO → AR	.41	0.06	6.83	<.001
CHO → AR	.28	0.08	3.62	<.001
CO → ANX	.15	0.07	2.14	<.001
IO → ANX	.39	0.05	7.21	<.001
CHO → ANX	.12	0.09	1.92	<.001
AR → REOI	.36	0.08	4.5	<.001
ANX → REOI	.42	0.07	6.0	<.001
CE×IO → REOI	−.21	0.06	3.5	<.001

Discussion

This study investigated the relationships between communication overload (CO), information overload (IO), choice overload (CHO), anxiety (ANX), anticipated regret (ANR), and resistance to external open innovation (ROI). Utilizing PLS-SEM analysis, several hypotheses were tested to understand the direct and moderating effects of cognitive engagement (CE) on these relationships. The findings provide nuanced insights into how these factors interact and influence each other.

The positive influence of communication overload on anticipated regret (ANR) among Tanzanian SMEs has been supported by the results (path coefficient = 0.159, t = 2.865, p = .004). This aligns with Eppler and Mengis, who discussed that an excess of information and communication channels can overwhelm individuals, leading to regret. More recent studies corroborate this finding (Eppler & Mengis, 2004). For instance, a study by Huang et al. demonstrated that the vast amount of information in digital communication can cause users to feel regretful due to missed or misinterpreted information (Huang et al., 2024). Similarly, Cao and Sun found that individuals often regret their communication decisions when faced with an overload of messages and information which they cannot adequately process (Cao & Sun, 2018). Hypothesis H1b suggested a positive relationship between communication overload and anxiety (ANX), which was not supported (path coefficient = 0.087, t = 1.456, p = .145). This finding contrasts with earlier research by Li et al., who indicated that communication overload could lead to increased anxiety levels (Li et al., 2024). The discrepancy may be attributed to various factors, including different contexts and measures of anxiety used across studies. Recent research provides mixed evidence on this relationship. For example, LaRose et al. found that while communication overload can lead to stress and anxiety, the impact varies significantly depending on individual coping mechanisms and the context of communication (LaRose et al., 2010). Conversely, a study suggested that the cognitive control over communication channels and message management can mitigate the negative outcomes associated with communication overload (Whelan et al., 2017). Moreover, contextual factors play a critical role. In work environments where employees have high autonomy and control over their job communication tools, the anxiety resulting from communication overload can be significantly reduced. This suggests that the impact of CO on ANX is not uniform and can be influenced by situational and individual differences. Therefore, Tanzanian SMEs should equip their staff with the ability and resources to control external communication. (Huang et al., 2024) supported information overload effects, while Cao and Sun (2018) focused on communication overload. The weaker role of communication overload in this study may reflect lower digital communication density in Tanzanian SMEs compared to firms in developed economies.

Hypotheses H2a and H2b, which proposed that IO positively influences ANR and ANX respectively, were both supported (H2a: path coefficient = 0.125, t = 2.238, p = .025; H2b: path coefficient = 0.224, t = 3.874, p = .000). These findings are consistent with previous studies that stated that excessive information leads to cognitive strain and negative emotions (Rutkowski & Saunders, 2018; Zhang et al., 2021). However, H2c, which suggested a direct positive relationship between IO and ROI, was not supported (path coefficient = 0.066, t = 1.144, p = .253). This suggests that while IO can increase regret and anxiety, it does not directly lead to resistance to open innovation.

Furthermore, this research investigated the relationship between CHO and ANR. Hypothesis H3a, which posited a positive relationship between CHO and ANR, was supported (path coefficient = 0.190, t = 3.468, p = .001). This supports the findings of previous studies, which argued that having too many choices can lead to decision fatigue and regret (Buturak & Evren, 2017; Park & Jang, 2013). On the other hand, H3b, which suggested a positive relationship between CHO and ANX, was not supported (path coefficient = −0.037, t = 0.634, p = .526). This is contrary to a previous study (Nagar, 2016) which affirms that CHO affects ANX. This finding suggests that while too many choices can lead to regret, it does not necessarily increase anxiety in organizational contexts. Too many choices can increase frustration and trigger employees into making wrong choices. In the context of Tanzanian SMEs, this study rightly affirms that CHO promotes ANR but does not necessarily cause ANX. This means that Tanzanian SME organizations must invest in methods that helps SMEs handle loads of choices professionally without causing negative emotions among their employees.

Hypotheses H4 and H5 posited positive relationships between ANR and ROI, and ANX and ROI respectively. Both were supported (H4: path coefficient = 0.141, t = 2.442, p = .015; H5: path coefficient = 0.129, t = 2.163, p = .031). These results align with Tzini, who suggested that negative emotions such as regret and anxiety can influence decision-making processes and lead to resistance to change (Tzini & Jain, 2018). In the context of health behavior, a previous study (Brewer et al., 2016) asserts that ANR can breed negative emotion which includes anxiety. Other studies (Abraham & Sheeran, 2003; Kumano et al., 2021) agree on this. In addition, the current study found that ANR has a positive effect on ROI. This implies that ANR among Tanzanian SMEs causes firms to resist external open innovative practices. They would rather depend on internal sources due to familiarity and fear of making a wrong decision while considering innovative external resources. This finding is consistent with previous studies (Ogbanufe & Pavur, 2022; Sherman et al., 2004; Valor, 2020) that affirmed that anticipated regret positively affects resistance. SMEs in Tanzania should be motivated to adopt policies that enhance the process of decision making when considering external sources or influence. This will enable SMEs handle better the stress or overload of work pressure on their staff members.

The study also examined the moderating effects of CE on the relationships between ANR, IO, and ROI. H6a, which proposed that CE moderates the relationship between ANR and ROI, was not supported (path coefficient = –0.027, t = 0.476, p = .634). However, H6b, which posited that CE moderates the relationship between IO and ROI, was supported (path coefficient = −0.108, t = 2.003, p = .045). This indicates that higher cognitive engagement can reduce the negative impact of information overload on resistance to external open innovation among Tanzanian SMEs. This consistent with previous studies (Dause & Kirby, 2019; Gorospe & Edaniol, 2022; Shaules et al., 2020), which suggest that cognitive engagement can help individuals effectively achieve positive and reduce negative emotional responses. In recent times, information has gone digital. Enterprises that do not go with the current information trends will either be overwhelmed with regret or left behind. As Information and computer technology (ICT) and now the integration of artificial intelligence boosts the speed at which information is available, firms that do not engage cognitively to handle this level of technology will be at loss. This finding is important given the current situation in Tanzania, where technological literacy is very low. It is therefore, important that firms must adopt cognitive trainings in terms of the effects of IO on ROI. Cognitive engagement, including technological literacy trainings on how to process loads of information and communication emerges as a potent solution for the problems currently faced by Tanzanian SME entrepreneurs. This moderation effect highlights the value of CE in reducing ROI, therefore SMEs should adopt cognitive trainings to reduce resistance ROI which causes a firm to lose potential values offered from outside the firm.

Moreover, study integrates the Stimulus–Organism–Response (SOR) framework with the empirical findings. Specifically, the overload constructs—Communication Overload (CO), Information Overload (IO), and Choice Overload (CHO)—represent the stimuli; regret and anxiety represent the organism states; and resistance to external open innovation represents the response. The results show that IO significantly predicted both regret and anxiety, confirming the SOR pathway from overload to emotional strain and ultimately resistance. However, the unsupported paths from CO → ANX and CHO → ANX suggest that in the Tanzanian SME context, communication and choice overload are less likely to generate anxiety—possibly due to lower digital communication intensity and more limited decision alternatives compared to developed economies. These findings refine the SOR model by showing that not all overload stimuli operate equally across contexts.

The integration between the conceptual framework and the empirical analysis is reinforced by the findings of this study. As illustrated in Figure 4, the Stimulus–Organism–Response (SOR) model was applied to explain how overload factors influence SME resistance to external open innovation. Information overload emerged as the strongest stimulus, leading to regret and anxiety, which subsequently predicted resistance outcomes. In contrast, the non-significant effects of communication overload and choice overload on anxiety point to boundary conditions in the model that are shaped by the Tanzanian SME context. Aligning the conceptual framework with these empirical insights offers a coherent and context-specific interpretation of how cognitive overload dynamics contribute to resistance to open innovation.

Figure 4.

Summary model integrating conceptual framework and key findings.

In terms of generalizability, the findings should be interpreted with caution. Overload–resistance dynamics may vary significantly across sectors: for example, manufacturing SMEs may encounter communication and information overload differently compared to service-oriented SMEs that rely heavily on customer interactions and digital platforms. This suggests that sectoral context shapes how stimuli translate into resistance responses. Future research should examine these dynamics across industries and in other geographic settings to strengthen external validity.

Conclusion and Research Implications

Conclusion

This study aimed to explore the intricate relationships between communication overload CO, IO, CHO, ANX, ANR, and resistance to external open innovation ROI among SMEs in Tanzania. By employing PLS-SEM analysis, we tested several hypotheses to understand the direct and moderating effects of cognitive engagement CE on these relationships. The findings provide significant insights into how these factors interact within the context of Tanzanian SMEs. Key results include the confirmation that communication overload positively influences anticipated regret, aligning with existing literature that suggests excessive communication can lead to feelings of regret due to overwhelming information. Information overload was found to positively influence both anticipated regret and anxiety, suggesting that an excess of information can lead to cognitive strain and negative emotions. Choice overload was shown to positively affect anticipated regret but not anxiety, suggesting that too many choices can lead to decision fatigue and regret without necessarily increasing anxiety levels in the context of Tanzanian SMEs. This highlights the need for these SMEs to manage the range of choices available to employees to minimize regret. Both anticipated regret and anxiety positively influenced resistance to external open innovation, underscoring the significant role of negative emotions in hindering innovation adoption. This implies that SMEs in Tanzania may resist external innovative practices due to fear of making wrong decisions and the associated negative emotions. The study also examined the moderating role of cognitive engagement, finding that higher cognitive engagement can reduce the negative impact of information overload on resistance to external open innovation. This underscores the importance of enhancing cognitive engagement, including technological literacy, among SME employees to manage information overload and reduce resistance to innovation.

This study highlights the relationships between communication, information, and choice overload, and their emotional and behavioral consequences in the context of Tanzanian SMEs. Also, it provides novel evidence on how cognitive overload mechanisms influence resistance to open innovation among Tanzanian SMEs—an underexplored context in prior literature. By situating the findings within the Stimulus–Organism–Response framework, the study extends theoretical discussions on innovation resistance and provides context-specific insights for organizational practice in emerging economies. The findings emphasize the importance of managing these overloads and enhancing cognitive engagement to foster a more innovative and resilient business environment. This research contributes substantial insights for policy makers, researchers, and concerned stakeholders. For Tanzanian SMEs, this means adopting policies and training programs that improve employees’ ability to handle information and communication effectively, thereby reducing negative emotions and resistance to innovation. Future research should continue to explore these relationships in different contexts and with additional variables to understand the dynamics at play further.

Theoretical Implications

This study significantly contributes to the adoption of external open innovation. It also contributes to the versatility of the SOR theory thereby advancing the theoretical understanding of the investigated factors concerning the resistance to external open innovation within the context of SMEs, particularly in Tanzania. The findings expand the use of SOR theory by highlighting distinct impacts of overloads on anxiety and regret, revealing that communication and choice overload primarily contribute to anticipated regret, while information overload affects both anxiety and regret. This differentiation enriches theoretical frameworks by integrating emotional responses into the understanding of innovation resistance, showing that negative emotions are substantial barriers to adopting external innovations. Moreover, the study introduces cognitive engagement (CE) as a moderating variable that mitigates the adverse effects of information overload on innovation resistance, emphasizing the importance of cognitive resources in managing overload and promoting adaptive organizational behavior. By contextualizing these theories in Tanzania, a developing economy, the research highlights the variability in overload dynamics across different economic and cultural settings, suggesting that resources and coping mechanisms differ significantly from those in developed contexts. Additionally, the implicit emphasis on technological literacy, a form of CE as a factor in managing information and communication overload, suggests its integration into theoretical models. The findings also differentiate the effects of specific overload types, such as the impact of choice overload on regret but not anxiety, providing a more detailed understanding of how various overloads influence emotional outcomes on organizational level. This mapping reinforces the Stimulus–Organism–Response (SOR) framework, where overload factors (stimuli) trigger regret and anxiety (organism states), which then drive resistance (response). These insights extend theoretical understanding in innovation management, organizational behavior, and SME research fields. Overall, this study enriches theoretical frameworks by incorporating emotional and cognitive dimensions, emphasizing context, and introducing new moderating factors like cognitive engagement, thus offering a comprehensive understanding of external innovation adoption in SMEs.

Practical Implications

This study offers Tanzanian SMEs several valuable takeaways. First, it emphasizes how crucial it is to control communication and information overload to lessen regret and worry, which might impede the acceptance of outside innovations. SME managers should have procedures to expedite communication channels and select pertinent data to avoid overload. The detrimental consequences of overload can be lessened by training initiatives that improve staff members’ capacity to handle and comprehend information. Furthermore, the research indicates that furnishing lucid directives and frameworks for decision-making may mitigate the decision exhaustion resulting from an abundance of options. This may result in more assured and successful decision-making regarding chances for external innovation. SME owners should create methodical procedures for assessing and choosing outside collaborators so staff members can handle options.

The results further highlight the contribution of cognitive engagement to decreasing resistance to innovation. SMEs should fund cognitive training and development initiatives that improve staff members’ technical literacy and problem-solving abilities. SMEs can encourage a more accepting attitude toward outside innovations by enhancing employees’ capacity to interact with and comprehend new information. According to the study, managing emotional reactions to overload is critical. Offering employees support mechanisms, like counseling or stress management courses, can help them deal with the remorse and worry that comes with having too much knowledge and too much communication. Organizational resilience can be enhanced by establishing a supportive work environment that recognizes and tackles these emotional difficulties.

Lastly, it is important for SME managers to understand how context-specific elements, such the state of the economy and culture, influence how overload affects resistance to innovation. Developing a culture of innovation and efficiently managing overload will require solutions specifically tailored to Tanzanian SMEs’ requirements and circumstances. By implementing these doable strategies, SMEs can more effectively manage the complexity of communication, information, and choice overload, eventually increasing their potential for innovation and expansion.

Limitations and Direction for Future Research

Despite being comprehensive, a few limitations need to be noted. First, because the data was only gathered from Tanzanian SMEs, the conclusions cannot be applied to SMEs in other areas or nations. To improve the external validity of the findings, future studies would benefit from a more varied sample of SMEs from different industries and geographic regions. Furthermore, the research depended on self-reported information, which biases like recollection or social desirability could influence. Future research could include objective measurements or triangulate data from several sources to reduce these biases and offer a more thorough knowledge of the phenomenon being studied.

Additionally, while this study focused on the moderating role of cognitive engagement, other potential moderators or mediators, such as organizational culture, leadership style, and technological infrastructure, were not explored. Future research could examine these issues to provide a more comprehensive view of how SMEs can effectively manage overload and stimulate innovation.

Lastly, the speed at which technology is developing and the growing incorporation of AI into company operations imply that communication, information, and decision overload will likely evolve. Future research should consider the changing technological landscape and its implications for SMEs, examining how emerging technologies can be employed to manage overload and enhance innovation capabilities.

Footnotes

ORCID iD

Leticia Matinde Ryoba

Funding

The authors received no financial support for the research,authorship,and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Data Availability Statement

Data will be made available at the request.

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Exploring Organizational Barriers to Embracing External Open Innovation: A Study of Resistance Factors in the Tanzanian SME Landscape

Abstract

Plain Language Summary

Keywords

Introduction

Theoretical Underpinning

Hypotheses Development

Stimulus (S)

Organism (O)

Response (R)

Moderating Roles

Conceptual Framework

Methods

Data Collection

Participants Demographic Analysis

Measurement Instruments

Data Analysis

Results

Construct Validity and Reliability

Validity

Reliability

Multicollinearity Test

Discriminant Validity

Structural Model Evaluation

Direct Hypotheses Testing

Moderation Analysis

Model Fit

Discussion

Conclusion and Research Implications

Conclusion

Theoretical Implications

Practical Implications

Limitations and Direction for Future Research

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

Data Availability Statement

References