1 Introduction

Recent strategy and innovation management literature highlight the growing importance of combining both exploitative (or incremental) and explorative (or radical) innovations to sustain high levels of organisational performance (Wong et al. 2017; ** innovation ecosystems (Inoue 2021), and ensuring diversity of innovation networks (Zhang et al. 2020a).

This review aims to identify core determinants for managing IA in organisations, systematically capturing research trends and the current state of literature, and methodically using insights from the review to make recommendations for future IA research. This article employs the systematic literature review methodology, which is popular in management research, because the methodology aids in addressing particular research questions on topical management issues through the use of well-defined protocols and processes that reduce the possibility of bias (Kraus et al. 2020, 2022). Using insights from the IA determinants, the review proposes a multi-level ‘wheel’ model of IA management that summarises the key findings. The model advances knowledge by presenting the main determinants and core management priorities from analysing the studies. The main argument in this multi-level model is that IA depends on core determinants within organisations and that these determinants influence management strategies for IA. Thus, ‘steering the wheel’ of determinants enables organisations make trade-offs in management priorities for realising IA. The proposed model of determinants and priorities presents a scope of aspects that seek to address the demand for a more comprehensive assessment of IA, which current research explains in the form of a paradox that permits the understanding of multiple-level and overlap** ambidextrous innovation aspects (Tan et al. 2017; Berraies et al. 2019; Lin and Qu 2021).

This review contributes to existing strategy and innovation management theory and attempts to fill the gap in knowledge on determinants for managing IA in two distinctive ways. First, the review provides new critical insights into the core determinants (i.e., enabling, and inhibiting factors) for managing IA. Second, and with close links to the first contribution, the study captures research trends on the extant literature concerning the management of IA, highlighting the current range of methodologies, use of management theories, and investigated sectors by IA researchers. Motivated by the aim, focus and contributions, this study confronts the following question:

What are the research trends and main determinants for managing IA in literature?

2 Innovation ambidexterity: a background

Fundamentally, ‘ambidexterity’ refers to the ability to perform two distinct tasks concurrently (He and Wong 2004). In an organisational context, the term ambidexterity refers to an organisation’s dual capabilities, or specifically its capacity to both expand external resources as well as integrate and use existing resources to intentionally gain competitive advantage in a dynamic and demanding environment (Duncan 1976). It also refers to an organisation’s ability to concurrently achieve alignment and flexibility at the business unit level (Gibson and Birkinshaw 2004). From the perspective of organisational learning, March (1991) proposed two distinct exploration and exploitation behaviours pertaining to ambidexterity. Here, the author describes exploration as an organisation's actions that try out a new option even though the outcomes are frequently unexpected, unfavourable, and immediate. In contrast, exploitation is the process of improving and develo** current capabilities, technologies, and paradigms, which results in a gradual and slight improvement of existing products. Organisations that are equally adept at exploring and exploiting are described as ambidextrous (Simsek 2009) and Benner and Tushman (2003) used this dichotomy of actions to categorise organisational innovation into exploratory and exploitative forms. However, these two endeavours compete for the same limited resources, which has often led to organisations choosing one over the other (March 1991). Consequently, managers must find ways to make the most of a company’s resources so that the company can engage in and operationally pursue both types of activities with equal success (Durugbo et al. 2021; Lin et al. 2013).

Based on an organisational perspective, IA is defined as the ability of organisations to “simultaneously pursue both explorative (discontinuous or radical) and exploitative (incremental) innovation” (Junni et al. 2013.p.299). This ability has been argued as the most effective strategy for enhancing business performance (Açıkgöz et al. 2021; Altındağ and Bilaloğlu Aktürk 2020), growth (Choi et al. 2021; Kuo et al. 2018; Liu et al. 2019a, b; Zhang et al. 2019), internationalisation (Alayo et al. 2021; Hsieh et al. 2019), sustainability (Zhang and Zhang 2016) and competitive advantage (Lin and Cheung 2022; Martin et al. 2017; Pangarso et al. 2020a, 2020b; Sijabat et al. 2020, 2021; Wang and Fang 2021; Ye et al. 2018a; Yu and Kim 2020). However, to successfully deal with the paradox of IA, organisations must invest in the development of innovative capabilities (Berraies et al. 2019). Based on this point of view, it is necessary to place greater emphasis on the antecedents that contribute to the development of ambidexterity to resolve the conflict that exists between exploratory and exploitative innovations, which compete for limited resources and are based on different information processing skills.

According to Berraies et al. (2019) organisations that master IA, i.e., those that can combine exploratory and exploratory innovation, are the most successful organisations. Thus, most organisations face challenges when trying to find a balance between the contradictory practices and logics that underlie exploration and exploitation. Growing tensions pull the company, teams, and individuals in opposite directions, which leads to an increase in frustration (Andriopoulos and Lewis 2010). Yet, exploratory and exploitative innovations are both increasingly essential for the success of an organisation (Gupta et al. 2006; Weigel et al. 2022), and focusing on just one form of innovation may result in a ‘failure trap’ (caused by too much exploration) or a ‘success trap’ (caused by too much exploitation) (March 1991). Accordingly, Bedford et al. (2019) argued that ambidexterity is one of the most complicated challenges and ambidextrous organisations that strive to acquire capabilities during exploration and exploitation, are able to make real product and service changes, but this is not always easy or problem-free. In addition, according to Chen and Liu (2020), to achieve IA, organisations usually face significant obstacles and tensions. Thus, organisations must effectively manage the determinants that aid in simultaneously adapting to changing environments and maintaining stability.

Even though IA determinants by characterisation play an important role in IA, the range of IA determinants in literature remains unclear, and more research is needed into how organisations enact IA in terms of processes and factors. Moreover, Asif (2017), notes that there is little research on the range of antecedents, determinants, factors, and relations of ambidexterity. Awareness of such determinants remains significant for cultivating the organisational structures, processes, and behaviours that permit and sustain IA. Although related reviews elaborate on specific contexts for IA in relation to organisational structures, processes, and behaviours (Zhao et al. 2021; Niewöhner et al. 2021; Zheng 2018; Aldianto et al. 2021), this review is unique in its concentration of IA determinants. Accordingly, this article is original and valuable in its focus on IA determinants and theories, and we seek to complement these existing reviews with insights that enrich discourse on innovation enablers and inhibitors as well as potential future research agendas. In so doing, this review strives to deepen knowledge and advance management research for IA.

3 Review methodology

Methodologically, the approach adopted is a systematic review, which Fink (2005), describes as a strategy to recognise, analyse, and synthesise the current body of final, documented work by researchers, academics, and practitioners in a systematic, clear, and reproducible way. The approach is chosen for this review because it supports the use of prior studies to develop knowledge that serves as a firm foundation for improving theory, addressing research gaps, and identifying research priorities (Kraus et al. 2022; Webster and Watson 2014). Systematic reviews also provide solid, integrated, and up-to-date understanding of concepts, as well as highlight major issues and trends in research output. For this review, we adopt a three-stage approach based on previous suggestions (Furlan et al. 2001; Petticrew and Roberts 2006; Booth et al. 2012), to aid in the search, selection, assemblage, extraction, and critical appraisal of relevant research publications, based on the review’s research question, i.e., ‘what are the research trends and main determinants for managing IA in literature?’.

The first stage is planning, which involves defining the study goal, research question, keyword list, and inclusion and exclusion criteria. Supporting this phase is a search strategy based on inclusion and exclusion criteria (Furlan et al. 2001; Petticrew and Roberts 2006). Scopus and Web of Science databases were adopted for the review to identify, screen, and select publications for the study. Web of Science is the oldest and authoritative database of scientific publications (Birkle et al. 2020), while Scopus is widely recognised as the world’s largest abstract and citation database of peer-reviewed literature, including scientific journals, conference proceedings, and books (Chadegani et al. 2013; Agapiou and Lysandrou 2015). Both databases provide an overview of the world’s research output in different academic disciplines. Overall, the review’s search strategy seeks to identify closely relevant sources for the review by focusing on literature with key terms in their titles and abstracts.

The second stage is executing, which focuses on conducing the review search by sourcing and gathering pertinent publications. Figure 1 summarises the sourcing approach adopted for this review.

Fig. 1
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Sourcing method

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We conducted database searches on Scopus and Web of Science for articles published until the end of 2021 with titles containing the terms “innovation ambidexterity” or “ambidextrous innovation”. The initial search with keywords found 152 and 102 records, respectively. Filtering based on the inclusion and exclusion criteria reduced the results to 132 and 87 articles on Scopus and Web of Science, respectively. The criteria centres on limiting sources to English-language journal articles and excluding conference papers, book chapters, and reviews. Reviewing the full text of the selected articles, and cross-referencing for duplicates, narrowed down the sources to 127 papers, and a subsequent re-evaluation of these sources resulted in a final number of 121 journal articles. The review contains contributions from scholarly journals such as Journal of Business Research, Journal of Construction Engineering Management, International Journal of Innovation Management, International Small Business Journal: Research Entrepreneurship, Chinese Management Study, Industry and Innovation, and International Journal of Operations and Production Management.

The third stage is analysing, which entails reading and analysing the body of literature in line with the research aim. This stage categorises and clusters the studied literature into general themes that present different determinants, outcomes, and management strategies for IA. Analysis focuses squarely on the 121 articles in relation to research designs, theories, antecedents, behaviours, and consequences of IA. Driving this stage is a thematic analysis that identifies major themes and arranges/ structures the examined literature under these themes (Dixon-Woods et al. 2005). Thematic analysis aligns with the systematic review and comprises data reduction that is accomplished in three stages (Guest et al. 2012). First, repeatedly reading the publications (twice for this review) for preparedness to identify possible themes and patterns within the reviewed articles and for familiarity with the data to gain deeper understanding of content. Second, generating initial codes that reflect concepts related to the study question. Third, creating themes through marking different and relevant sentences along with rereading content to confirm and contrast different themes.

Following the data reduction stages, an assessment of the reliability and validity of created themes is performed, which is a critical step in ensuring the themes reflect the entire text (Alhojailan and Ibrahim 2012). For reliability and internal validity of themes, two independent researchers reviewed the documents containing the developed themes. Additionally, defining inclusion and exclusion criteria in advance helps to reduce the risk in this review. The research also evaluates external validity, primarily in terms of the review's scope, which is limited to peer-reviewed scientific literature. Threats to this study include potential gaps between research findings and recommendations, as well as various procedures that might convey clues intentionally or subconsciously during study selection and influence the review process's conclusions. As a result, it is critical to recognise the potential consequences of these risks to validity when evaluating the study findings. The next section presents findings from the reviewed articles.

4 Review findings

This section presents the findings of the review in accordance with the review question. It details research trends and determinants for managing IA based on an analysis and synthesis of the literature.

4.1 Research trends on managing innovation ambidexterity

4.1.1 Research methodologies and yearly trends

The basis for this review is the 121 articles published between 2007 and 2021. Figure 2 and Table 1 show the yearly distribution of the reviewed articles, indicating a growing trend and interest in the topic, particularly in the past 7 years.

Fig. 2
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Breakdown of reviewed articles according to year published

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Table 1 Yearly distribution for review according to citations
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In terms of methodologies, the analysis showed that the earliest and main approaches used in studies are surveys with 94 articles (77.7%) that focus mainly on gathering cross-sectional data. From 2015 onwards, there have been qualitative case studies and approaches present in 11 articles (9.1%), and econometric analysis of panel data in 8 articles (6.6%), as shown by the yearly distribution of Fig. 3 and Table 2. During the last three years, two other methodologies have emerged, i.e., mathematical models and simulation in 5 articles (4.1%), and mixed approaches that combine surveys and interviews in 3 articles (2.5%).

Fig. 3
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Yearly breakdown of reviewed articles according to research methodologies

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Table 2 Overview of review methodologies in reviewed articles
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4.1.2 Theories

Insights from the reviewed articles suggest various theoretical underpinnings for IA studies, as summarised by Table 3. Dominating the literature are resource theories with studies based on the resource-based view, knowledge-based view, resource dependency and dynamic capability theories. From an earlier study on the impact of learning capability (Lin et al. 2013), the scope for resource-based analysis extends to topics such as business intelligence (Božič and Dimovski 2019), resource allocation mechanism (Fu et al. 2021), and entrepreneurial orientation (EO) (Arzubiaga et al. 2018; Nofiani et al. 2021). The next set of theories are leadership theories which posit on leader behaviour and structural mechanisms facilitating IA, with most coverage by upper echelon theory that examines executive viewpoints on organisational strategic choices for IA. Other theories applied to study the impact of leadership styles and characteristics on IA are transformational, transactional, habitual domain, ambidextrous, strategic forms of leadership theories.

Table 3 Overview of main theories in reviewed articles
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Organisational theories posit on processes by organisations and contain recent expositions based on organisational learning theory and the Technology–Organisation–Environment framework, while the cluster of information theories postulate on organisational exchanges and flows with instances of transaction cost and information processing theories. Role-based theories involve stakeholder theory (Ardito et al. 2020) and stewardship theory (Arzubiaga et al. 2018), and there are other theoretical groundings based on institutional theory in relation to institutional pressures for IA (Chang and Gotcher 2020; Song and Zhao 2021) and componential theory of creativity in regards to entrepreneurial leadership (Khairuddin et al. 2021). The review also contains applications of social capital theory for analysing resource configurations (Choi et al. 2021) and for examining the effects of business and political ties (Zhang and Cui 2017) and managerial ties (Li et al. 2014; Zhang et al. 2019).

4.1.3 Geographical regions and industry sectors

The analysis indicates variations in the industry sectors investigated in the reviewed articles, as summarised by Fig. 4. The most investigated single-sourced sector was the technology industry with reported work in 24.8% (29 out of 121) of the reviewed articles. Manufacturing and service firms are favoured sources of data as reported by 17.4% (21 out of 121) of articles. The review includes 9.9% (12 out of 121) of studies involving small and medium enterprises (SMEs) and 2.5% (3 out of 121) of studies with educational institutions. Agricultural, construction, gaming, hotels, ship**, and pharmaceutical firms each had coverage in 2 studies (representing 8.5% of included articles). The review contains eight investigations (representing 6.6% of included articles) of the automotive, finance, healthcare, restaurants, fashion, property development, paper and pulp, and utilities sectors. Studies with multiple sectors make up 24.8% (30 out of 121) of articles.

Fig. 4
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Breakdown of industry sectors in the reviewed articles

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Next, the breakdown of reviewed articles according to geographical regions, as illustrated by Fig. 5, shows that mainland China is the most studied region with 36.4% (44 out of 121) of the reviewed articles. Next is Taiwan at 9.9% (12 out of 121), followed by Spain and Indonesia each at 5.0% (6 out of 121), Tunisia at 4.1% (5 out of 121), Italy at 3.3% (4 out of 121), and the United Kingdom and United States each with 3 studies, each representing 2.5% (3 out of 121) of the reviewed articles. Australia, Brazil, France, Japan, Mexico, Pakistan, Romania, Scotland, and Turkey offer 2 studies each, representing 15.3% (18 out of 121) overall of the reviewed articles. Austria, Finland, Hong Kong, India, Ireland, Jordan, Kenya, Korea, Netherland, Norway, Slovenia, Thailand, and Vietnam each had 1 study, accounting for 10.7% (13 out of 121) overall of the reviewed articles.

Fig. 5
figure 5

Breakdown of geographical regions in the reviewed articles

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4.2 Main determinants for managing innovation ambidexterity

Our analysis of the reviewed articles suggests seven main themes on determinants for managing IA. First is a ‘process mechanisms’ theme, which involves 47.1% (57 from 121) of the reviewed articles followed by an ‘organisational learning’ theme covered by 22.3% (27 from 121) of the articles. Next theme of interest in the review is a ‘leadership styles’ theme investigated in 13.2% (16 from 121) of the articles and the themes of ‘technology investments’ and ‘organisational contexts’, representing 5.8% (7 from 121) and 4.1% (5 from 121) of the articles, respectively. The remaining themes are ‘environmental uncertainties’, and ‘institutional pressures’ covered in 4.1% (5 from 121) and 3.3% (4 from 121) of the articles, respectively. Figure 6 and Table 4 communicate the yearly trends and citations for these determinants, and the following subsections define the fundamental concepts within the determinants.

Fig. 6
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Yearly breakdown of reviewed articles according to determinants for managing IA

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Table 4 Main themes on determinants for managing innovation ambidexterity according to citations
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4.2.1 Process mechanisms

Generally, the most studied category of IA determinants is the theme named ‘process mechanisms’, which underscores procedures, tendencies, and with emphasis on managing IA processes. Driving these interests, are six concepts, which studies apply as antecedents, moderators, and mediating variables, as shown by Table 4. These concepts are interaction, involvement, collaboration, networks, capabilities, and orientation.

Studies with focus on interaction as a process mechanism reveal that this concept influences companies' ability to create ambidextrous innovations and thus improve their performance. For instance, studies on the interaction modes of IA (McDermott and Prajogo 2012; Lucena 2016) and influencing factors of balanced organisational IA (Cho et al. 2020; Lin and Qu 2021) reveal a significant and positive impact of IA on performance, as well as factors such as entrepreneurial bricolage (Lin and Qu 2021) that can balance innovation. Other studies, which focus on variables such as buyer–supplier interaction (Wang et al. 2021) and supplier-side search (Wang et al. 2019) suggest that ambidextrous innovation and performance are positively influenced by these variables. Similarly, Zhang et al. (2016) in a study on how EO and human resource management interact to influence IA, discovered that such interaction has a considerable impact on IA and, as a result, firm performance. Integration mainly concerns co** combination (i.e., alignment and misalignment) issues (Chen et al. 2020) and combining dimensions of IA (Suzuki 2015; Dunlap et al. 2016). Networks facilitate exchanges and resource flow for IA through avenues such as social media networks (Scuotto et al. 2020), strategic networks (** normative frameworks for digital interdependence in the context of organisational IA.

5.2 Organisational legacy

The next challenge relates to research on ‘organisational legacy’ (from reflections on the organisational learning determinant) for improved understanding on the transferability of learning capabilities and lessons learnt from individual to individual, ensuring the preservation of organisational knowledge. Organisational legacy progresses resource and organisational theories, as shown by Table 3, and this proposed track for research further challenges researchers to examine legacy systems “that are mission critical, expensive to maintain, brittle and inflexible to changes, run on obsolete hardware, incomplete or outdated documentation, and difficult to extend and integrate with other systems” (Gholami et al. 2017; p.101). In addition to maintaining organisational expertise and contributing positively to the organisation’s income and growth, legacy systems give a considerable competitive advantage (Sneed 1995; Erlikh 2000). Despite their importance in sustaining daily operations, legacy systems can impede innovation efforts (Bakar et al. 2021) and the failure of such systems, might have disastrous consequences for the organisation (Khadka et al. 2014). Hence, to ensure that these systems continue to deliver the best service possible in accordance with global trends, there must be support, integration, or modernisation of such systems. Modernisation of legacy systems is crucial when the maintenance of the old systems is insufficient to satisfy new and emerging expectations. Modernisation refers to improvements of existing systems to interface with newer technology while emphasising agility to adapt quickly to business changes (Ahmad et al. 2021). According to Khadka et al. (2014) there are numerous studies on legacy systems, yet only a few investigations have focused on the entire process of modernising old systems. Therefore, we advocate for future research studies on potential links that exist between organisational legacy, learning, and IA. Likewise, we recommend examinations of the process of modernising legacy systems that enable IA and the critical success criteria for this approach. Future studies could also investigate the implementation processes for modernising a legacy system in support of IA, both theoretically and experimentally.

5.3 Stewardship behaviour

For management researchers, there are future opportunities to examine ‘stewardship behaviour’ (mainly from reflections on the leadership styles determinant), a behaviour which instils organisational leaders with not just personal goals, but also collectivist and pro-organisational motivations (Davis et al. 1997). With an emphasis on responsibility and accountability concerned with the long-term implications of actions (Nunn and Avella 2015), stewardship advances leadership styles and strategies via the motivation of employees that boosts participation and inspiration for innovation. Insights from the reviewed articles suggest the influence of different leadership styles on IA underpinned by various leadership and role-based theories, as shown by Table 3. Despite this focus on leadership, the literature offers little insights on the possible role of stewards in enhancing IA. Although a study (Arzubiaga et al. 2018) applies stewardship for explaining the varied impacts of boards of directors on the link between EO and ambidextrous innovation within family SMEs, treatment in the wider context of organisations remains limited. Future research could study specific roles of stewardship for IA in different organisational contexts, i.e., formalisation, structure, creativity, and culture, as identified from the organisational context determinant. Although, empirical evidence suggests links between stewardship behaviour and the success of innovation (Domínguez-Escrig et al. 2019), there are opportunities for studies to test this relationship in normative and cognitive organisational contexts. In addition, future research may examine the issues of stewardship in IA and their influence on the various stages of the innovation process.

5.4 Technology sourcing

The fourth challenge relates to research on ‘technology sourcing’ (from reflections on the technology infrastructure determinant) with opportunities to examine the process of R&D outsourcing, the engagement of various types of partners in collaborative networks, and the negotiation processes with contractors for the formulation and implementation of various IT contracts, licenses, staff, leases, assets. Accordingly, technology sourcing studies advance the management of infrastructure investment and technology. This management focus entails overseeing the creation, deployment, and reconfiguration of resources within organisations, in accordance with resource, organisational, and information theories presented by Table 3. Technology sourcing has become a crucial part of a company’s technology strategy due to the continually evolving and complicated nature of technology. Advances in the speed and sophistication of technology motivates organisational strategies for purchasing and procuring technologies from outside sources (Tsai and Wang 2009). In this context, organisations also turn to outside partners to aid with innovation and technology management processes. With the increasing importance of innovation as a key enabler for a company’s competitive advantage, a number of studies examine the issues surrounding technology sourcing in relation to foreign direct investment (De Propris and Driffield 2006), mergers and acquisitions on corporate (Cefis 2010), innovative capability (Zhao et al. 2005) and innovation performance (Tsai and Wang 2009). Though these studies highlight the impact of technology sourcing on organisational innovation, much remains unexplained in the specific context of IA. Thus, we urge for research investigating the process of technology sourcing that facilitates IA and critical success factors of this process. Additionally, future research could hypothesise on and empirically examine implementation mechanisms for technology sourcing in IA.

5.5 Organisational resilience

Another challenge for future research involves analysing organisational resilience (from reflections on the organisational context determinant). Organisational resilience entails making ongoing adjustments and adaptation to tough situations and disruptions. In this context, organisational resilience enables firm to react and recover from socio-economic shocks and to maintain a desired degree of stability. Research concerning organisational resilience identifies several abilities that contribute to resilience, e.g., fixing and learning from mistakes quickly (Weick and Sutcliffe 2001), and changing business practices to suit the needs of the new environment (Mafabi et al. 2012), with continuous innovation playing a critical role for organisational survival. Particularly, a resilient organisation gathers information from the environment to implement innovations related to achieving resilience in times of crisis and calm (Durugbo and Al-Balushi 2022). Furthermore, failure to become resilient may cause organisations to lose their vision, mission, and authorisation, making them more vulnerable to deterioration and abandonment. Consequently, research on organisational resilience advances organisational contexts in line with organisational and resource theories of Table 3. Due to recent socio-economic shocks and crisis like COVID-19 pandemic and the financial crisis, organisational resilience continues to gain substantial academic attention. Yet insights on the topic from the perspective of IA remain restricted. As a result, we challenge academics to study this area empirically to develop, recognise, and harness the potential for building organisational resilience within the framework of IA. Studies may also consider more specific research questions for future IA studies on organisational resilience such as ‘how can IA be developed through organisational resilience?’ In addition, there is a need to investigate, understand, and eventually operationalise the interface between organisational resilience and IA.

5.6 Environmental readiness

Another potential research area entails studies of ‘environmental readiness’ (mainly from reflections on the environmental uncertainty determinant), which refers to the external factors that drive an organisation to seek IA. Research studies focused on innovation adoption reveal that environmental readiness along with technological, organisational readiness are all essential for the adoption of innovation (Yang et al. 2015a; AlSheibani et al. 2018). In this sense, environmental readiness refers to how organisational users are prepared and eager to accept innovation in response to perceived external influences. These forces include customer/supplier pressure, competition pressure, and external support, all of which impact adoption (Priambodo et al. 2021). Furthermore, research shows that the adoption of innovation is influenced by external variables such as competitive pressure and regulatory issues (Ifinedo 2005). Considering these viewpoints from previous research, environmental readiness offers a construct that tackles environmental uncertainty and dynamism, with theoretical underpinnings from resource, organisational, and institutional theories of Table 3. Thus, understanding future needs of environmental readiness by organisations in times of uncertainty and crisis, such as the COVID-19 pandemic, continues to be a concern, since it determines organisational continuity and viability in dynamic business environments (Priambodo et al. 2021). Research studies could also consider readiness constructs for contexts, such as sustainability, interconnectivity, and security, with respect to IA by organisations.

5.7 Institutional transformation

The final challenge is for research studies on ‘institutional transformation’ (primarily from reflections on the institutional pressure determinant). Here, institutional transformation refers to the process of change that is inherent in the act of organising and this process is carried out by institutional actors as they manage, innovate, and modify their routines practices through time (Orlikowski 1996). Such organising indicates major shifts in organisational operations, which necessitate structural, management, and cultural changes (Sligo et al. 2019). Institutional transformation responds to institutional pressures for IA (AlMalki and Durugbo 2022; 2023), as well as fosters process mechanisms for IA, in line with the resource and institution theories presented by Table 3.

Like challenges for organisational resilience studies, research on institutional transformation demands focus on recent socio-economic shocks and crises like COVID-19 pandemic and the financial crises, as well as major technological transitions and transformations in society. Here, prospects exist to unravel how organisations apply IA in response to these on-going institutional shocks and transitions. Current research underscores the need for evolutionary views on institutional transformation (Karaulova et al. 2017), and such stances could serve as the foundation for wider critiques on the potential organic nature of constructs for managing IA. Alternatively, research could examine the role of institutional transformation on IA and shed light on IA relative to megatrends (e.g., digitalisation, globalisation, and personalisation) of modern society.

6 Conclusions

Balancing exploitative and explorative innovation, i.e., innovation ambidexterity (IA) remains an essential condition for delivering competitive advantage and seeking out new revenue streams. Consequently, insights on the key determinants and management strategies for IA are crucial to managing the inherent conflict and paradox that exist between exploitative and explorative innovations. These determinants and strategies contribute to the development of IA within organisations and necessitate review on an on-going basis to update scholarship and practice. Kee** this in mind, this review addressed the following research question: ‘What are the research trends and main determinants for managing IA in literature?’.

Using insights from 121 peer-reviewed journal articles published between 2007 and 2021, the review finds seven determinants for managing IA: (i) process mechanisms, (ii) organisational learning, (iii) leadership styles, (iv) technology investments, (v) organisational contexts, (vi) environmental uncertainties, and (vii) institutional pressures. Reinforcing these determinants are resource, leadership, organisational, information, role-based, creativity, institutional and social capital theories that influence organisational-, individual-, process-, and environmental-level structures and behaviours for IA.

This review has two major limitations. First, the scope of the review is limited to identifying the main determinants for managing IA. In view of this limitation, there is a need for more data on the activities of innovation processes, the behaviour of intra- and inter-organisational actors, and organisational configurations for IA. Second, the review method is limited to a systematic approach with thematic analysis of the main concerns and topics of studies. As a result, deeper insights based on other review methodologies, such as meta-analyses and meta-syntheses, can provide more focused and extensive knowledge on constructs, dependencies, and links between variables within qualitative and quantitative studies of IA. Further research on co-citations may also provide insights into the nature of citation dynamics and potential links between articles. Sourcing for the review centres on limiting search results to English-language journal articles, excluding conference papers, book chapters, and grey literature. Furthermore, the initial search for the review uses keywords ‘innovation ambidexterity’ or ‘ambidextrous innovation’, and there is potential for additional insights using related keywords such as ‘explorative and exploitive innovation’ and ‘radical and incremental innovation’.

In line with insights on the seven determinants, the review posits on seven management priorities of process integration, organisational diversity, leadership control, technology infrastructure, organisational culture, environmental dynamism, and institutional environments. Correspondingly, the determinants serve as the backdrop for seven areas of future management research involving digital interdependence, organisational legacy, stewardship behaviour, technology sourcing, organisational resilience, environmental readiness, and institutional transformation. In summary, the review anticipates that the necessities and niceties of these proposed areas will aid in strengthening existing knowledge on IA and in uncovering new and exciting phenomena, as organisational managers develop and implement strategies based on the combinatory and contradictory contexts of IA.