Effect of System on Post-Merger and Acquisition Performance of Commercial Banks in Kenya

Effect of System on Post-Merger and Acquisition Performance of Commercial Banks in Kenya

¹Doreen Mushira, ²Dr Daniel Wanyoike, ³Dr. James Kahiri, ⁴Dr. Allan Mugambi

¹PhD Candidate, Jomo Kenyatta University of Agriculture and Technology

^2,3,4Lecturer, Jomo Kenyatta University of Agriculture and Technology, School of Business and Entrepreneurship, Nairobi, Kenya.

DOI: https://dx.doi.org/10.47772/IJRISS.2024.809098

Received: 04 September 2024; Accepted: 10 September 2024; Published: 04 October 2024

ABSTRACT

This study examines the effect of System on post-merger and acquisition (M&A) performance of commercial banks in Kenya, using the McKinsey 7S model as a framework. System, serving as the independent variable, is operationalized through knowledge mapping and assessment, knowledge transfer and retention, and continuous improvement. Post-M&A integration, the moderating variable, is measured by the degree and rate of integration. The study is anchored on the Resource-Based View and Universalistic Best Practices theories. Out of the 38 commercial banks in Kenya, 29 have undergone M&As. A sample of 10 banks, involved in M&A activities for a period ranging from six months to five years, was selected. This timeframe recommended by Masoud et al., (2020) is critical for M&As as it allows the assessment of both short-term and medium-term integration outcomes, where initial challenges are addressed and operational synergies start manifesting. Data was collected using both primary and secondary sources. Primary data, analyzed through content and framework analyses provided qualitative insights into integration practices. Secondary data, primarily consisting of financial ratios of bank performance (Return on Assets, Return on Equity, Return on Investment, Operating Profit Margin, and Net Profit Margin) was analyzed using the Independent Sample T-test in SPSS version 28.0. Correlation analysis and inferential statistics were employed to measure the strength of relationships between study variables. The study employed a 95% confidence level with a 5% level of precision, ensuring that the results were robust and could be generalized with minimal error. These levels of precision and confidence enhance the reliability and quality of the research by reducing the likelihood of making incorrect inferences about the broader population. The findings demonstrate that knowledge mapping and assessment, knowledge transfer and retention, and continuous improvement are essential for smooth post-M&A integration, safeguarding critical knowledge, and maintaining the merged entity’s competitiveness. The results further confirmed that the conceptual framework accurately predicted post-M&A performance, with System having a positive, statistically significant effect. Additionally, post-M&A integration was found to moderate the relationship between System and post-M&A performance, amplifying the positive outcomes when the integration was well-executed. The study recommends the adoption of a comprehensive artificial intelligence (AI) strategy, focusing on augmenting human capabilities in M&A integration processes rather than replacing them. Future research should explore the impact of regulatory changes and market conditions on post-M&A performance, particularly how evolving regulations, competition, and macroeconomic factors influence outcomes in the banking sector.

Key words: System, Mergers and Acquisitions, Knowledge mapping & assessment, Knowledge transfer & retention, Continuous improvement, Post-M&A Performance, Post-M&A Integration.

BACKGROUND OF THE STUDY

Global Mergers and Acquisitions (M&As) generate high earnings through volume and value, posturing them as enablers of corporate scale, globalization, sustainability, competitiveness, stability, and innovations (Vanwalleghem, Yildirim, & Mukanya, 2020; Berger, Ghoul, Guedhami, & Roman, 2017; Mohamed, 2022; Giessner, Horton, & Humborstad, 2016; Nandi & Nandi, 2017). In 2021, global M&A bank deals recorded unprecedented returns of $ 5.1 trillion (PWC, 2022).  Despite the high value and popularity, failure and under-performance still persist (Mohamed, 2022; Vanwalleghem et al., 2020; Berger et al., 2017). Scholars estimate a 50% – 90% failure rate (Naraločnik & Bertoncelj, 2016; Sedlacek & Valouch, 2018; Masoud, Buzovich, & Vladimirova, 2020; McKinsey & Company, 2019; EY, 2024) attributed to strategic and financial factors (Masoud et al. 2020; Sedlacek & Valouch, 2018; Graebner, Heimeriks, Huy, & Vaara, 2017; McKinsey & Company, 2019) such as strategic fit, culture clash, regulatory hurdles, over-estimating synergies, and insufficient due-diligence (Berger, et al., 2017). Lace and Kirikova (2021) allude to the influence of systems on post-M&A performance and success.

The government of Kenya through Sessional Paper No.2 of 2012, and the Basel III regulatory framework ensured a sound and resilient financial system (Ochieng et al., 2021), that sparked major re-organization in the banking sector through M&As. The sector has experienced 63 M&A activities, yielding to 38 commercial banks currently operating in Kenya, 29 of which have undergone M&As (CBK, 2023). Capital adequacy requirement of Basel III framework accelerated Kenya Commercial Bank’s acquisition of National Bank of Kenya; and the cross-border acquisition of Fidelity Bank by the SBM holdings of Mauritius (Nguli & Kyule, 2020).

Need for growth and expansion of technological network ushered Kenya’s M&A wave (Ochieng et al., 2021). Technology oriented banks accelerated performance through reduced costs, enhanced efficiency, and new product development (Deloitte, 2022). The merger between Commercial Bank of Africa (CBA) and National Industrial Credit Bank (NIC) was technologically oriented (Nguli & Kyule, 2020). In a bid to acquire greater financial power, influence, and tax advantages, the Equity Group adopted aggressive geographical expansion through regional acquisitions in Uganda, South Sudan, Zambia, Mozambique, Tanzania, Rwanda, and Congo (Nguli & Kyule, 2020).

Portfolio diversification gained traction in the financial sector as evidenced through the acquisition of K-Rep Bank (Sidian) by Centum investment to gain presence in commercial banking (Ochieng et al., 2021). Equatorial Bank’s acquisition by Mwalimu Sacco enabled the Sacco collect deposits beyond its membership, access funds at lower rates, venture into trade finance and offer ATM services to its members (Nguli & Kyule, 2020). Even though M&As have saved financially struggling banks, the overall performance of Kenyan banks remains unsatifactory. While some banks benefit from the synergy, others struggle with their dipping performance indicators (Ochieng et al., 2021). Such failures are linked to poor strategic fit between merging entities and insufficient due diligence by the acquirer (Nguli & Kyule, 2020).

Trust bank limited that had previously undergone a merger collapsed in 1998 after a series of financial challenges occasioned by insolvency and financial mismanagement (CBK, 2023). The case of Trust bank is cited as one of the major banking failures in Kenya’s financial history, emphasizing the importance of stronger regulatory frameworks.  Ten banks (Delphis Bank, Ambank, Dubai Bank, Mayfair Bank Ltd, Transnational Bank, Imperial Bank, Jamii Bora bank, Spire bank, National Bank, and Sidian Bank) struggled with capital adequacy issues and underwent further M&A activities for survival (CBK, 2023). Although the Kenyan economy staggered in the first quarter of 2021 due to the COVID-19 pandemic, with a decline of almost 5.7%, banking services recorded an increase in growth; the Third Quarter Report indicated a recovery in economic activity, hence, recording an improvement from –5.5% to -1.1% (Ochieng et al., 2021).

Objectives Of The Study

This study was guided by the following objectives:

Specific Objectives 

• To examine the effect of System on post-M&A performance of banks in Kenya;
• To evaluate the impact of the moderating effect of Post-M&A integration on the relationship between System and post-M&A performance of commercial banks in Kenya.

Reearch Hypothesis

This study hypothesized that:

H_01: System has no statistically significant effect on post-M&A performance of banks in Kenya;

H_02: Post-M&A integration has no statistically significant moderating effect on the relationship between System and post-M&A performance of banks in Kenya.

THEORETICAL FRAMEWORK

Resource Based View (RBV)

The RBV of a firm postulated by Barney in 1991 holds that in strategic management, the fundamental sources and driver to firms’ competitive advantage and superior performance are mainly associated with the attributes of their resources and capabilities which are valuable and costly-to-copy (Barney, 1991). Barney asserts that a resource must fulfill the ‘VRIN’ criteria (Valuable, Rare, Inimitable, Non-substitutability) to provide competitive advantage and sustainable performance. This theory elaborates firm-specific perspective on why firms succeed or fail at the market place. By applying RBV, we gain insights into how well the banks’ strategic resources and capabilities are leveraged and integrated post-M&A. This knowledge offers broad understanding of overall success / challenges faced by the banks post-M&A. This theory supports the System and post-M&A performance variables of the study.

Universalistic Best Practice Theory (UBPT)

Universalistic theory postulated by Hodgetts and Luthans (2003) holds that there is a set of superior / best strategic processes whose adoption unavoidably leads to superior firm performance. This theory supports the post-M&A integration moderating variable of this study through the notion that certain ‘best’ management practices yield to firm performance. Proponents of this theory perceive certain firm practices as better suited for improvement of performance. Thus, adoption of high-performance practices of strategic management constitutes an excellent path to achievement of increased firm value, efficiency and performance principles. The UBPT demonstrates how adherence to universal best practices during post-M&A integration serves as a moderating variable, potentially enhancing the performance of commercial banks by ensuring that integration processes are managed effectively and consistently.

Conceptual Framework

A conceptual framework is the total, logical orientation and associations of anything and everything that forms the underlying thinking, structures, plans, practices and implementation of an entire research project (Kivunja, 2018). The conceptual framework of this study theorizes the interrelationship of Style (independent variable), Post-M&A performance of commercial banks in Kenya (dependent variable), and the moderating effect of Post-M&A Integration on the relationship between System and Post-M&A Performance.

Figure 1.1 Conceptual Framework  

Source: Author

System

The system element refers to processes and procedures that govern how work is accomplished in a firm. System encompasses information technology, financial, and other operational processes that support organizational activities. The effectiveness of these systems significantly impact firm efficiency, adaptability, and overall performance (McKinsey & Company, n.d.). Lace and Kirikova (2021) assert that organizational systems include everything from the company’s hierarchy and reporting relationships to its information technology infrastructure and decision-making processes. Systems facilitate coordination, communication, and decision-making within an organization (Lace & Kirikova, 2021). Bodner and Capron (2018) assert that in the context of M&As, the integration of firm systems is essential for harmonizing operations of the merging entities, streamlining processes, and achieving synergies. Failure to align organizational systems results to inefficiencies, duplication of efforts, and conflicts, ultimately undermining the success of the M&A. Merging firms need to ensure that their systems are compatible and can work together seamlessly to avoid disruptions in operations. A study by Lee et al., (2023) highlights the importance of creating robust knowledge management systems to capture and share valuable knowledge, as well as the challenges of overcoming barriers to knowledge flow across the merged entity.

Knowledge mapping and assessment

Knowledge is a critical asset that enables organizations to make informed decisions, develop innovative products and services, and gain a competitive advantage (Lace & Kirikova, 2021). Knowledge mapping involves identifying, visualizing, and documenting the knowledge assets of an organization, including explicit knowledge such as patents, information flows, and processes, as well as tacit knowledge such as expertise and relationships (Bajwa, Kitchlew, SairShahzad, & Shahzad, 2017). By mapping out the intellectual capital of both the acquiring and acquired firms, managers can gain better understanding of strengths and weaknesses of each entity, and identify areas for knowledge sharing and collaboration, ultimately leading to more effective decision-making and resource allocation (Najaf & Najaf, 2018).

Assessment practices, involve evaluating the quality, relevance, and accessibility of the knowledge assets identified through the mapping process. This can include assessing the level of expertise of key employees, the effectiveness of existing processes and systems, and the alignment of knowledge assets with the strategic goals of the organization. By conducting a thorough assessment of the knowledge assets of both organizations, managers can identify potential gaps and redundancies, and develop strategies to leverage the combined knowledge base for maximum post-M&A performance Bajwa et al., (2017).

Knowledge transfer and retention

Knowledge transfer (KT) entails movement of tacit knowledge that resides in an individual’s head, and explicit knowledge, which is recorded in reports, documents, articulated and captured among staff (Vieru & Rivard, 2015). Knowledge transfer describes movement of knowledge among teams, divisions, organizations, rather than individuals (Najaf & Najaf, 2018). Transfer of knowledge refers to both sharing and pursuit of knowledge. Studies on knowledge demonstrate that to gain competitive advantage, organizations need to understand how to transfer expertise and knowledge from experienced employees to novices (Sdahsiah & Sepahvand, 2017). Sarala et al., (2017) found that KT in M&As is an important source of competitive advantage, defined as successful exchange and absorption of knowledge.

On the other hand, knowledge retention refers to the process of preserving and transferring critical knowledge, skills, and expertise from one organization to another during an M&A transaction (Mathrani & Edwards , 2020). In the context of commercial banks, knowledge retention is essential for maintaining customer relationships, ensuring operational continuity, and driving innovation. The loss of key employees and their knowledge can lead to disruptions in service delivery, decreased productivity, and loss of competitive advantage (Grossman, 2018).

Continuous improvement

Continuous improvement is a systematic approach to enhancing firm performance through incremental changes in processes, products, or services (Demirer & Karaduman, 2023). In the context of M&A activities, continuous improvement facilitates the integration of disparate systems, cultures, and operations (Bodner & Capron, 2018). By fostering a culture of innovation and learning, continuous improvement enables banks to identify and address operational inefficiencies, streamline processes, and enhance customer satisfaction. Moreover, continuous improvement can help banks adapt to changing market conditions, regulatory requirements, and technological advancements, thereby improving their competitive position in the industry (Alsharo, Gregg, & Ramirez, 2017; Howard, Steensma, Lyles, & Dhanaraj, 2016; Grossman, 2018; Vieru & Rivard, 2015).

Empirical Review

Knowledge Management and Post-M&A Performance

Using descriptive research design, Naisiae and Gitari (2018) conducted a study among Nakuru County’s manufacturing firms and confirmed a positive significant relationship between strategic knowledge management practices and firm performance. The study found that knowledge transfer, application and management policy had a statistically significant positive influence on organizational innovation. Knowledge transfer was significantly and positively correlated to firm innovation.

Focusing on Kenyan Commercial banks, Gakuo and Rotich (2017) conducted a study on the effect of strategic knowledge management on performance. The study used descriptive research design and data was collected from a sample of 116 management staff. The study concluded that knowledge management processes capability was key in achieving performance of commercial banks in Kenya. The findings further affirmed that knowledge management processes promote knowledge acquisition in commercial banks in Kenya.

Using cross-sectional research design, Kombo et al., (2015) conducted a study to examine whether knowledge strategy affected firm innovation. The study targeted 655 manufacturing Kenyan firms. Structured questionnaires were administered to managers. Results showed that knowledge strategy positively and significantly affected firms’ innovativeness.

RESEARCH METHODOLOGY

This study was based on the descriptive research design. Out of the 38 registered commercial banks currently operating in Kenya, 29 have undergone M&As. A sample of 10 banks, involved in M&A activities for a period ranging from six months to five years, was selected. This timeframe recommended by Masoud et al., (2020) is critical for M&As as it allows the assessment of both short-term and medium-term integration outcomes, where initial challenges are addressed and operational synergies start manifesting. One hundred and forty-four (144) key informants holding decision-making positions in the banks, and M&A advisors who were involved in respective banks’ M&A processes participated in the study.

Data was collected using both primary and secondary sources. Primary data was collected through a semi-structured questionnaire. The reliability of the questionnaire items was determined using the Cronbach’s alpha coefficient. A Cronbach coefficient >0.70 determined the reliability of the data collection tool. Primary data was then analyzed through content and framework analyses. These methods provided qualitative insights into integration practices. Secondary data consisting of financial ratios of bank performance (Return on Assets, Return on Equity, Return on Investment, Operating Profit Margin, and Net Profit Margin) was analyzed using the Independent Sample T-test in SPSS version 28.0. Correlation analysis and inferential statistics were employed to measure the strength of relationships between study variables. The study employed a 95% confidence level with a 5% level of precision, ensuring that the results were robust and could be generalized with minimal error. These levels of precision and confidence enhance the reliability and quality of the research by reducing the likelihood of making incorrect inferences about the broader population. The research data was presented through inferential analysis, percentages, measures of central tendency, and measures of dispersion. The study adopted correlation and linear analyses as the inferential statistical tools, and the findings were presented in graphs and tables.

Regression Analysis

Regression determines if the predictor variables explain the criterion variable. The multiple regression model for primary data was: –

Equation 1 without Moderator:          Y = β₀ + β₁X₁ + εi……………….….…. (1)

Equation 2 with Moderator:               Y = β₀ + β₁X₁ + βmM + εi………….…. (2)

Whereby:

Y –       Post-M&A performance;

B₀ –      Intercept coefficient or value of dependent variable when the

Independent variable is zero.

εi –      Error term (extraneous / stochastic variables);

X₁ –     System

βmM – Post-M&A Integration

β₁, and βmM – Regression coefficients

For secondary data, the multiple regression model was: –

Y = α₀ + α₁X₁ + α₂X₂ + α₃X₃+ εii……………………………………….……. (3)

Y –       Net Profit Margin

α₀ –       Intercept coefficient or value of dependent variable when all the

Independent variables are zero.

εii –     Error term (extraneous / stochastic variables);

α₁ –      Return on Assets

α₂ –      Return on Equity

α₃ –      Operating Profit Margin

α₁, α₂, α₃, – Regression coefficients

RESEARCH FINDINGS AND DISCUSSIONS

Knowledge Mapping and Assessment

Figure 1.2 Knowledge Mapping Approaches

Figure 1.2 presents study findings indicating that 11.85% of the banks utilized knowledge audits to facilitate smooth transition during mergers and acquisitions (M&A) to ensure; valuable knowledge assets were preserved, gaps were addressed, and synergies were leveraged. These findings are corroborated by Ayinde et al. (2021), and Makambe (2015) who found that knowledge audits during change, support firms in facilitating integration, reducing knowledge loss, streamlining processes, improving decision-making, and supporting change management. Expert interviews were adopted by 13.33% to gain insights and domain-specific knowledge, identify key knowledge-holders, and enhance knowledge maps. A study by Castro and Moreira (2023) established that expert interviews provide depth, accuracy, and context, which are essential for developing effective and meaningful knowledge maps. Process mapping was utilized by 16.30% to understand knowledge creation and dissemination, optimizing workflows and identifying inefficiencies. A study by Castro and Moreira (2023) established that process mapping in organizations is critical since it integrates, optimizes, and standardizes processes, ensuring smoother transition and more efficient operations.

Knowledge mapping workshops (15.56%) promoted a culture of knowledge sharing, continuous learning, and improved decision-making. These observations were corroborated by Vladimir et al., (2019) who opined that knowledge mapping workshops were essential for facilitating collaboration, identifying and addressing knowledge gaps, and ensuring a smooth and effective integration of knowledge resources. Technology-based mapping (14.81%) empowered banks to manage knowledge maps effectively, leading to improved decision-making, innovation, and organizational performance. These findings are supported by several studies undertaken by management scholars (Chankoson, Chen, Wang, & Wang, 2023; Dai, Xu, & Chen, 2018; Hakimah & Ibrahim, 2018; Hakim & Sensuse, 2018; Balaid, Abd Rozan, Hikmi, & Memon, 2016), who found that technology-based mapping enhances the efficiency, accessibility, and effectiveness of knowledge management processes within a firm. It also supports integration, organization of knowledge resources, ultimately contributing to firm success.

Social network analysis (16.30%) provided deeper insights into knowledge sharing dynamics within banks, leading to more effective knowledge management strategies and improved firm performance. Ribeiro et al., (2017) found that social network analysis provides valuable insights into the relationships and flows of knowledge within an organization. Cultural assessments (11.85%) enriched knowledge mapping efforts by creating a supportive cultural context, and enhancing knowledge management. These observations are corroborated by several management scholars (Kartika, 2019; Aziz Abdul & Muhammad , 2018; Rahman, Partiwi, & Theopilus, 2021) who hold that cultural assessment aids in integration of diverse cultures, knowledge flow and collaboration, identification of tacit knowledge, mitigating resistance to change, enhancing retention of key talent and knowledge, supporting creation of a unified knowledge management strategy, promoting innovation and synergy. They further observe that cultural assessments provide a deeper understanding of how knowledge is created, shared, and stored within organizations; helping to bridge cultural gaps and ensure the success of knowledge mapping efforts.

Knowledge transfer and retention

Banks use various metrics to evaluate the efficacy of knowledge transfer and retention. Findings in Figure 1.3 indicate that 10.37% respondents emphasized the importance of a high knowledge retention rate for M&A success. Banks that prioritized knowledge retention experienced improved performance post-deal. This observation is corroborated by Anggoro and Simamora (2019), and Aggestam et al., (2014), who observe that the knowledge retention rate directly influences an organization’s ability to transfer knowledge efficiently and retain it for future use. Higher retention rates ensure that knowledge is not only preserved but also effectively shared across the organization, enabling better decision-making, innovation, and productivity.

Figure 1.3 Knowledge Evaluation Metrics

Employee engagement in knowledge sharing activities, cited by 11.11%, drove post-M&A performance by leveraging human capital and enhancing innovation. This finding was supported by various management scholars (Kaldeen, Thelijjagoda , & Samsudeen, 2021; Onyango, Egessa, & Ojera, 2022; Cheruiyot , Kimutai, & Kemboi, 2017; Annika, 2017; Juan, Ting, Yao, & Kweh, 2018; Escriba-Carda, Canet-Giner, & Balbastre-Benavent, 2023) who posit that employee engagement in knowledge sharing is critical and significantly impacts knowledge transfer and retention in an organization. They opine that employee engagement and knowledge transfer spurs staff motivation to share knowledge, experiences, expertise and insights with colleagues. They scholars observe that employee engagement and knowledge retention reduce turnover in knowledge loss, preservation in organizational memory, and reduces knowledge silos. Further, the scholars state that together, these factors create a culture that supports continuous learning, innovation, and the preservation of valuable expertise.

Knowledge accessibility, cited by 15.56%, ensured employees had access to relevant information for informed decision-making. When knowledge is easily accessible, employees can conduct thorough research, verify facts, and cross-reference information, leading to more robust and reliable evaluations (Kirkeby, 2015). Time taken to access critical information, cited by 14.07%, directly affected post-M&A performance and decision-making. Lace and Kirikova (2021) propound that the time taken to access critical information directly influences the speed and effectiveness of knowledge transfer, as well as the organization’s ability to retain and apply that knowledge over time. Faster access supports better decision-making, enhances collaboration, preserves institutional memory, and promotes innovation. On the other hand, delays can lead to knowledge loss, frustration, inefficiencies, and reduced organizational agility.

Knowledge contribution and creation, cited by 11.11%, spurred post-M&A success by maximizing creativity and innovativeness of human capital and fostering collaboration. By providing opportunities for employees to contribute their expertise and creativity, firms can maximize the benefits of M&A transactions and achieve their strategic objectives more effectively (Bratianu, 2018). Knowledge utilization efficiency, cited by 17.04%, is essential for maximizing knowledge assets and improving operational effectiveness post-M&A.  Management scholars (Reagans, Miron-Spektor, & Argote, 2016; Zaim, Muhammed, & Tarim, 2019) opine that by prioritizing efficient knowledge utilization as part of their M&A strategy, organizations can achieve their integration objectives more successfully and realize sustainable performance improvements in the post-M&A period.

Meanwhile, 11.11% highlight customer satisfaction as crucial for providing feedback that informs improvements and drives revenue growth and competitive advantage post-M&A. By prioritizing customer satisfaction as a strategic objective post-M&A, organizations can enhance their ability to achieve integration objectives, realize synergies, and create sustainable value for all stakeholders in the integrated environment (Lee & Yew, 2022; Shete, 2021; Suchanek & Kralov, 2019)

Compliance and risk management, mentioned by 9.630% ensured that the banks adhered to regulations and standards, enhanced customer trust, and long-term success. This observation was corroborated by a study undertaken by Deloitte (2018), that emphasized prioritization of compliance and effective risk management practices post-M&A in ensuring risk mitigation and delivering superior customer experiences.  A report by IMF (2022) holds that compliance often mandates that certain types of data, such as financial records, contracts, or customer information, are retained for legal or audit purposes. Organizations undergoing change must ensure that this critical knowledge is stored in compliance with these regulations, ensuring that it remains accessible for future reference. Gutterman (2020) asserts that risk management frameworks ensure that retention policies are in place to safeguard important data, preventing inadvertent loss or unauthorized destruction.

Continuous Learning and Improvement

Findings captured in Figure 1.4 reveal that 8.889% cited that Leadership support was crucial in fostering a culture of learning and improvement, with studies (Khattak, Zolin, & Muhammad, 2020; Islam, Tariq, & Usman, 2018) showing that transformational leadership positively impacts employee commitment through job characteristics such as feedback, task variety, and decision-making autonomy.

Figure 1.4 Continuous Learning and Improvement

Learning and development programs cited by 10.37% play a key role in promoting critical thinking, open communication, collaboration, and knowledge sharing within organizations. These observations are supported by Halmaghi and Tomița (2023), and Movahhed (2023); who observe that by creating a learning culture, investing in development resources, encouraging feedback and experimentation, aligning learning with business goals, and fostering collaboration, leaders enable employees to continuously grow and adapt. In a firm where leadership prioritizes and supports learning, employees are more engaged, motivated to develop new skills, and better equipped to drive long-term success and innovation.

Knowledge sharing platforms cited by12.59%, are essential for influencing strategic, innovative, and marketing abilities, as well as creating a sustainable competitive advantage. These findings are supported by various studies (Deng & Lu, 2022; Kozhakhmet & Nazri, 2017; Chatterjee , Chaudhuri, & Vrontis, 2022; Mehmood, Jian, Akram, Akram, & Tranveer, 2022; Ahmad & Karim, 2019; Angels, Torben , Gooderham, Elter, & Hildrum, 2017), that observe that knowledge-sharing platforms foster continuous learning and improvement in organizations by providing centralized and accessible environment for employees to share, collaborate, and acquire knowledge. These platforms create a culture of transparency, collaboration, and innovation, which are essential for ongoing learning and growth.

Mentorship and coaching cited by 13.33% inspire and empower employees, increase productivity, and bridge skill gaps within the company.  Extant literature holds that good coaching and mentoring schemes are effective in raising self-direction, self-esteem, efficacy, and accomplishments (Serrat, 2017). Further, coaching and mentoring bridge the gap between existing skills of the employees and required skills for the company (Neupane, 2015).

Recognition and rewards programs cited by 8.889%, signal that a firm values its staff and incentivize behaviors that contribute to learning, innovation, and continuous improvement. When employees receive appreciation and recognition for their work, they reciprocate with a sense of obligation and respond with continued high performance (Madhani, 2020). Feedback mechanisms cited by 8.148% are essential for changing employee behavior and improving performance. Lampe et al., (2021) observe that feedback enables employees to interpret the appropriateness of their behaviors and take actions to improve their performance.

Continuous improvement mechanisms cited by 6.667% drives operational efficiency, quality management, innovation, and employee engagement post-M&A. Studies (Njoroge, 2022; Khan , Kaviani, Galli, & Pharma, 2019; Almaiman & McLaughlin, 2018; Gonzalez & Van Aken, 2016) show that continuous improvement mechanisms enable knowledge creation and sharing, innovation, organizational efficiency, cost and production synergies, risk management, and firm performance. Cross-functional collaboration cited by 9.630% is crucial for knowledge sharing, problem-solving, innovation, and process improvement in the integrated environment. These findings are corroborated by studies (Yin, Caldas, Oliveira, & Kermanshachi, 2023; Priyono, 2016) that cite operational efficiency, improved problem-solving, innovation and creativity.

Learning from mistakes cited by 11.11% contributes to skills development, risk mitigation, and organizational learning post-M&A. Researchers (Charlotte Weekly, 2021; Kucharska, 2020) affirm that learning from mistakes contributes to skills development, risk mitigation, organizational learning, and decision-making. Continuous monitoring and evaluation cited by 10.37% are critical for tracking progress, managing risks, ensuring quality assurance, and achieving integration objectives successfully. These findings are corroborated by Guerra-Lopez and Hicks (2013) who established that continuous monitoring and evaluation yields to enhanced organizational learning, proper resource allocation, and risk management.

Hypothesis Testing

The study sought to establish the effect of System on post-M&A performance of banks in Kenya. The following null hypothesis was drawn:

H_01: System has no statistically significant effect on post-M&A performance of banks in Kenya;

To test for this hypothesis, the following univariate regression model was adopted.

Y = β₀ + β₁X₁ + εi

Model summary, Analysis of Variance (ANOVA), and regression coefficients, were used to determine the rejection, and failure to reject the null hypothesis. Study findings are as indicated in Table 1.1.

Table 1.1 System and Post-M&A Performance

Findings in Table 1.1 indicate that the value of R² is 0.730, which implies that 73% of variation in post-M&A performance can be attributed to system; however, the remaining 27% variation in post-M&A performance of commercial banks can be attributed to other aspects other than system. The findings further illustrate that system and post-M&A performance are strongly and positively related as indicated by the correlation coefficient (R) value of 0.855. This finding is corroborated by a study (Li & Lin, 2019) which found that effective knowledge management practices positively influenced the integration process and post-M&A performance. Similarly, a study by (Chen & Huang, 2019) revealed that knowledge management capabilities significantly impact the innovation performance of merged entities.

ANOVA findings present a p-value of 0.000 < 0.05, an indication that the model is significant. The findings also show that the F-calculated value 360.190 is greater than the F-critical value (F_1,133 = 3.94); hence the model is reliable and can be used to predict post-M&A performance of commercial banks in Kenya.

The following model was fitted from the coefficients table:

Y= 0.867 + 0.588 X₁

From the equation, when systems are held to a constant zero, post-M&A performance of commercial banks will be at a constant value of 0.867. The findings also show that a unit increase in systems will lead to a 0.588 unit increase in post-M&A performance of commercial banks. The t-statistic 18.979 has a p-value = 0.000 < 0.05. Therefore, we reject the null hypothesis (H0₁) and conclude that system has a significant statistical effect on post-M&A performance of commercial banks in Kenya.

Ramadani et al., (2017) propound that innovation influenced by knowledge spillovers is inherently related to firm performance. Hence, these findings suggest that organizations that effectively manage systems during the post-M&A phase are more likely to achieve superior performance outcomes.

Moderated Summary Model

H_02: Post-M&A integration has no statistically significant moderating effect on the relationship between system and post-M&A performance of banks in Kenya.

To test for this hypothesis, the following multivariate regression model was adopted.

Y = β₀ + β₁X₁ + βmM + εi

Model summary, Analysis of Variance (ANOVA), and regression coefficients, were used to determine the rejection, and failure to reject the null hypothesis. Study findings are as indicated in Table 1.2.

Table 1.2 Moderated System and Post-M&A Performance

Findings in Table 1.2 show that the value of R² is 0.792, implying that 79.2% of variation in post-M&A performance can be attributed to System. However, the remaining 20.8% variation in post-M&A performance can be attributed to other aspects other than System. Evidently, the moderated R² (0.792) is greater than the unmoderated R² (0.730). Findings further illustrate that the moderated system and post-M&A performance are strongly and positively related as indicated by the correlation coefficient (R) value of 0.890, which is greater than the unmoderated correlation coefficient of 0.855. Extant literature propounds that the effectiveness of post-M&A Integration significantly moderates the outcomes of M&A Activities in organizations by influencing operational, financial, and strategic success (Hitt et al., 2018; Weber et al., 2019).

ANOVA findings had a p-value of 0.000 < 0.05, showing that the model is significant. The F-calculated value 251.186 > F-critical value (F_2,132 = 3.072); hence the model is reliable and can be used to predict post-M&A performance of banks in Kenya.

The following model was fitted from the coefficients Table:

Y= -0.918 + 0.282X₁ + 1.443M

When moderated system is held at a constant zero, post-M&A performance is maintained at -0.918. A unit increase in moderated system results to a 0.282 unit increase in post-M&A performance. The Beta value of the moderated system is 0.282, whereas the unmoderated Beta value of system is 0.588, suggesting that introduction of the moderating variable reduced the amount of variation in post-M&A performance that can be explained by the system variable. Since the T-statistic 5.039 has a p-value of 0.000 < 0.05, we reject the second null hypothesis (H0₂) and conclude that Post-M&A integration has a statistically significant moderating effect on the relationship between system and post-M&A performance of banks in Kenya.

Secondary Data Analysis

Independent Samples T-tests

Using the Levene’s Test, an independent samples T-test was undertaken on the financial ratios (Return on Assets, Return on Equity, Return on Investment, Operation Profit Margin, and Net Profit margin) to assess whether the means of the pre and post-M&A were different. The hypotheses of the T-test were as follows:

H_0: The variances of the pre and post M&A groups of the financial measures of performance are equal;

H_A: The variances of the pre and post M&A groups of the financial measures of performance are not equal.

Table 1.3 Group Statistics

Table 1.4: Independent Samples T-Test for Financial Measures of Performance

The Levene’s test assumes that the variance is equal across the groups (Pre and Post), of the individual financial measures of performance.

Effect of M&A on Return on Assets (ROA)

Based on secondary data captured in Table 1.4, the p-value 0.001 < 0.05 indicating that there is a significant difference in variance between the pre and post-M&A Return on Assets (ROA). We therefore reject the null hypothesis and adopt the alternative hypothesis that assumes unequal variance. Based on group statistics in Table 1.3, The means of the two groups (1.85 and 2.67) are statistically different. The calculated t-statistic of the alternate hypothesis is -2.640 at 27.978 df. When compared to the critical value the calculated t statistic is greater than the critical t-value ( -2.640 > + 1.701). The negative t-statistic of H_A suggests that the Mean of ROA at post-M&A is higher than that of pre-M&A, and also means that there was a significant difference in variance of pre and post-M&A ROA. The improvement in ROA signals enhanced asset efficiency; indicating that the merged entities are using their assets more efficiently to generate profits. This could be due to successful integration, elimination of redundancies, better utilization of existing assets, or streamlined operations that reduce costs and improve asset turnover.

A study by Jallow et al., (2017) reported a drastic reduction of about 8% of firm’s return of assets from pre-merger to post-merger; signaling a decline attributed to decline of management efficiency in employing available assets to generate earnings. On the other hand, Borodin et al., (2020) indicates that M&As can lead to improvements in return on assets (ROA), primarily driven by increased efficiency and cost savings. Therefore, this secondary data and extant literature indicate that the effect of M&A on ROA can be both positive and negative, depending on factors such as the strategic rationale behind the deal, execution of integration process, and the overall market conditions.

Effect of M&A on Return on Equity (ROE)

The p-value 0.009 < 0.05 indicating that there is a significant difference in variance between the pre and post-M&A Return on Equity (ROE). We therefore reject the null hypothesis and adopt the alternative hypothesis that assumes unequal variances. The means of the two groups (12.19 and 18.20) are statistically different. The calculated t-statistic of the alternate hypothesis is -2.583 at 27.993 df. When compared to the critical value the calculated t statistic is greater than the critical t-value ( -2.583 > + 1.701). The negative t-statistic of H_A suggests that the Mean of ROE at post-M&A is higher than that of pre-M&A, and also means that there was a significant difference in variance of pre and post-M&A ROE.

Improvement in ROE points to increased shareholder returns; suggesting that the merged entities are generating higher profits relative to shareholders’ equity. This could result from a combination of better profitability (due to improved operations or synergies), and possibly a reduction in equity (through share buybacks or financial restructuring), which enhances the returns on the remaining equity base.

Research demonstrates that M&A transactions can have a significant impact on a firm’s ROE. On one hand, M&A activities can lead to an increase in ROE by enabling companies to achieve economies of scale, access new markets, and diversify their product offerings (Entezarkheir & Sen, 2017; Tran, 2023). For example, a company that acquires a competitor may be able to reduce costs through synergies and increase its market share, leading to higher profits and a higher ROE.  On the other hand, M&A transactions can also have a negative impact on ROE. For example, if a company overpays for an acquisition or fails to integrate the acquired company effectively, it may experience a decrease in profitability and a lower ROE (Muhammad et al., 2019). Additionally, the financial leverage involved in M&A transactions can also impact ROE, as increased debt levels may lead to higher interest expenses and lower profits. Hence, the secondary data demonstrates that the effect of M&A on ROE can vary depending on the specific circumstances of the transaction and the ability of the companies involved to execute their strategic objectives.

Effect of M&A on Operating Profit Margin (OPM)

The p-value 0.019 < 0.05 indicating that there is a significant difference in variance between the pre and post-M&A Operating Profit Margin (OPM). We therefore reject the null hypothesis and adopt the alternative hypothesis that assumes unequal variances. The means of the two groups (6.10 and 8.60) are statistically different. The calculated t-statistic of the alternate hypothesis is -2.413 at 25.997 df. When compared to the critical value the calculated t statistic is greater than the critical t-value ( -2.413 > + 1.706). The negative t-statistic of H_A suggests that the Mean of OPM at post-M&A is higher than that of pre-M&A, and also means that there was a significant difference in variance of pre and post-M&A OPM. Improvement in the Operating Profit Margin (OPM) alludes to operational efficiencies. The rise in OPM indicates that the merged entities are more efficient at converting revenue into operating profit; likely due to cost-saving synergies, economies of scale, or improved operational management. This suggests that the banks are effectively managing their operating expenses post-M&A.

Effect of M&A on Return on Investment (ROI)

The p-value 0.000 < 0.05 indicating that there is a significant difference in variance between the pre and post-M&A Return on Investment (ROI). We therefore reject the null hypothesis and adopt the alternative hypothesis that assumes unequal variances. The means of the two groups (7.90 and 4.66) are statistically different. The calculated t-statistic of the alternate hypothesis is 2.968 at 22.471 df. When compared to the critical value the calculated t statistic is greater than the critical t-value ( -2.583 > + 1.717). The positive t-statistic of H_A suggests that the Mean of ROI at pre-M&A is higher than that of post-M&A, and also means that there was a significant difference in variance of pre and post-M&A ROI.

Decline in ROI despite improvement in ROA, and OPM, could be as a result of high M&A costs or integration. If the initial investment required for the M&As was substantial, the overall return on this investment could be diluted, particularly if the benefits take time to fully materialize. In the event that the M&A was heavily financed by debt, the cost of servicing this debt (interest expenses) could weigh heavily on ROI. Even though the banks are performing well operationally (reflected in higher ROA and OPM), the burden of debt reduces the overall return on the total capital invested. A decline in ROI could also result from changes in the banks’ capital structure post-M&A. If the merged entities issued more equity or took on additional liabilities, the overall returns might be spread across a larger base of invested capital, leading to a lower ROI.

Effect of M&A on Net Profit Margin (NPM)

The p-value 0.286 > 0.05 indicating the assumption of equal variance between the pre and post-M&A Net Profit Margin (NPM), hence we fail to reject the null hypothesis. The means of the two groups (9.60 and 14.77) are not statistically significant based on the p-value (0.286). The calculated t-statistic of the null hypothesis is -2.051 at 28 df. When compared to the critical value, the calculated t statistic is greater than the critical t-value ( -2.051 > + 1.701), thus suggesting there was no significant change in Pre and Post-M&A NPM.

A stable net profit margin (NPM) could be interpreted as revenue and cost equilibrium. The fact that NPM has not significantly changed suggests that, despite improved operating efficiency (higher OPM), the merged entities might be facing other expenses that stabilize the net profit including; interest expenses from debt financing, integration costs, or other non-operational expenses that off-set the gains in operating efficiency. Subsequently, the merged entities might be operating in a highly competitive environment where it is difficult to increase pricing power, thereby keeping profit margins stable despite improvements in other areas of the business.

Alternatively, the merged entities could be undergoing delayed synergy realization, or they could be reinvesting their operating profits into growth initiatives or strategic projects, which could reduce the immediate impact on NPM and ROI. While these investments may enhance long-term value, they can dampen short-term returns. While operational synergies are improving ROA and OPM, full financial benefits, including higher net profit margins, might take longer to materialize due to the time required for complete integration or market adaptation.

Regression Model Of Financial Ratios Of Performance

The following multiple regression analysis was undertaken to ascertain the effect of financial profitability ratios on post-M&A performance of banks in Kenya:

Y = α₀ + α₁X₁ + α₂X₂ + α₃X₃+ εii……………………………………………. (3)

Where Y is Net Profit Margin; α₀ is the Intercept coefficient or value of dependent variable when all the independent variables are zero; εii is the error term (extraneous / stochastic variables); α₁ is Return on Assets (ROA); α₂ is Return on Equity (ROE); α₃ is Operating Profit Margin (OPM). α₁, α₂, α₃, are regression coefficients.

Table 1.5 Model for Financial Measures of Performance

Findings in Table 1.5 show that the value of R² was 0.992 suggesting that 99.2% variation in Net Profit Margin (NPM) can be explained by Operation Profit Margin (OPM), Return on Assets (ROA), and Return on Equity (ROE). The remaining 0.8% suggests that there are other factors that can be attributed to variation in Net Profit Margin (NPM) that were not captured in the model. The correlation coefficient (R) indicates the relationship strength between the combined independent variables and the dependent variable. From the findings, the variables were strongly and positively related as indicated by R = 0.996.

ANOVA findings present a p-value of 0.000 < 0.05, an indication that the model is significant. The findings also show that the F-calculated value 254.902 is greater than the F-critical value (F_3,9 = 3.86); hence the model is reliable and can be used to predict post-M&A Net Profit Margin (NPM).

From the coefficients table, the following regression model was fitted:

Y = -13.448 + 1.759 X₁ + 0.209 X₂ + 8.421 X₃

The model equation stipulates that holding the variables ROA, ROE, and OPM to a constant zero, post-M&A Net Profit Margin (NPM) will be at a constant value of -13.448.  Findings further demonstrate that ROA has a positive significant effect on post-M&A NPM (β = 1.759, p = 0.002 < 0.05). Therefore, improvements in ROA results to an increase in post-M&A NPM by 1.759 units. ROE has a significant positive effect on post-M&A NPM (β = 0.209, p = 0.015 <0.05). Therefore, improvement in ROE results to an increase in post-M&A NPM by 0.209 units. OPM has a significant positive effect on post-M&A NPM (β = 8.421, p = 0.01 <0.05). Therefore, improvement in OPM results to an increase in post-M&A NPM by 8.421 units.

CONCLUSIONS OF THE STUDY

The System variable was operationalized by knowledge mapping and assessment, knowledge transfer and retention, and continuous improvement. Study findings revealed that System had a positive statistically significant effect on post-M&A performance of commercial banks in Kenya; hence the first null hypothesis was rejected. The correlation and regression results revealed that post-M&A integration moderated the relationship between System and post-M&A performance; hence, the second Null hypothesis was rejected.

Secondary data on the financial measures of performance indicates significant improvement in Return on Assets (ROA), Return on Equity (ROE), and Operations Profit Margin (OPM) post-M&A. However, there was a decline in Return-on-Investment (ROI), and no significant change on Net Profit Margin (NPM) post-M&A. This scenario reflects a complex post-M&A environment where the banks demonstrate strong operational performance (evidenced by improvements in ROA, ROE, and OPM); yet financial returns on the overall investment (ROI) are lower, and net profit margins remain stable. This could be due to the high costs associated with the M&A, increased debt servicing, or the banks’ respective strategic decisions to reinvest profits into future growth rather than boosting short-term returns. Hence, while the M&As have led to better operational efficiency and shareholder returns, the full financial benefits may take longer to manifest, particularly in terms of ROI and NPM.

RECOMMENDATIONS

The study recommends the adoption of a comprehensive artificial intelligence (AI) strategy, focusing on augmenting human capabilities in M&A integration processes rather than replacing them. Future research should explore the impact of regulatory changes and market conditions on post-M&A performance, particularly how evolving regulations, competition, and macroeconomic factors influence outcomes in the banking sector.

Contribution To Theory And Existing Knowledge

Validation of Theories

While existing literature often suggests that knowledge management is critical to M&A success; empirical studies specifically quantifying the impact of knowledge mapping, transfer, retention, and continuous improvement in the banking sector are less common. This study provides empirical evidence validating / challenging existing theoretical frameworks.

Holistic View of M&A Success

This study contributes to a more comprehensive view of what drives post-M&A success. By correlating knowledge management practices with key performance metrics such as profitability, market share, customer satisfaction, and risk management; the study illustrates the direct and indirect pathways through which knowledge influences M&A outcomes.

Sector-Specific Insights

The banking sector has unique characteristics, such as stringent regulatory requirements, the need for strong risk management, and the value of customer trust. This study would offer insights into how knowledge management processes tailored to these specific needs impact M&A outcomes, potentially leading to more nuanced theories applicable to financial firms.

Interdisciplinary Contributions

The findings could have implications beyond the banking sector, contributing to the broader fields of strategic management, organizational behavior, and knowledge management. Other industries undergoing M&A could apply similar frameworks to improve their integration processes.

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