International Journal of Operations Management
Volume 5, Issue 1, June 2025, Pages 7-20
Impact of Decision Support Systems on Strategic Management: A Meta-Analysis
DOI: 10.18775/ijom.2757-0509.2020.51.4001
URL: https://doi.org/10.18775/ijom.2757-0509.2020.51.4001Nikodemus Angula1, EliasKandjinga,2 Odilo Sikopo3, Fiina Shimaneni4, Martha Namutuwa5
Namibia University of Science and Technology (department of Governance and Management Sciences, Windhoek,Namibia)
Abstract: The purpose of the study was to examine the impact of decision support systems on strategic managementthrough the use of existing literatures related to the impact of decision support systems on strategic management. Most literature utilised for this study were within the period of 2015-2023 in order to reflect the recent developments of Decision Support System (DSS) and its impact on strategic management. For many business organisations DSS can be perceived as the implementation of system support tools that can aid organisational management in making informed decisions. As many organisations transform into strategic management, making informed decisions becomes more and more crucial, hence the implementation of DSS tools. Many organisations face challenges of conscious planning, the lack of strategic decision making and sharing information which makes it difficult in increasing profits hence organizations need to utilise its available resources and adopt appropriate decision-making tools to compete effectively.
DSS tools are essential in making decisions such as allocation of resources, contributions towards development of sustainable communities and economic growth, making it possible to achieve sustainable development goals (SDGs) for a living economy. The resource-based view (RBV) further illustrates the efficient utilization of organizations’ diverse resources to develop their capabilities. This aspiration calls for the implementation of strategic information planning systems that collect data for decision purposes. The implementation of DSS tools further helps the organisation to create a competitive advantage as the organisation becomes more turbulent to the volatile global world of business.
Keywords: Decision Support System, Strategic management, strategic information system
1. Introduction
The complexity of managing challenges in the business environment in our time is a stimulating factor that requires a different approach and concerted efforts for making informed decisions. The real-world decision-making situations in business are subjected to rationality of technical and economic evaluation elements to find alternative solutions that require decision support (Rashidi,Ghodrat,Samali&Mohammas (2018). According to Kitsios &Kamarioto (2018), for effective strategic management and decision-making it is essential to develop a Decision Support System (DSS) such as the strategic information systems planning (SISP) to collect information for decision makers support for strategy formulation and implementation. Strategic management is explained “as a discipline that examines the practice of management and governance at the level of the firm and the people who serve in that function” Cory (2024, p.3). On the other hand DSS is defined by Rashidi,Ghodrat,Samali&Mohammas (2018) “a system that intended to support decision makers in semi-structured problems that could not be completely supported by algo-rithms, Rashidi et al,. (2018). The objective of DSS is to be utilised as an enabler for managers to expand their capabilities but not to replace them, thus it will complement the decision making by managers for them not only to rely on personal experience and intuition.
However, the effectiveness of the decision support systems on business strategic decisions is unknown. According to Verina, Fauzi, Nsasari, Tanjung &Iriani (2018), the human capital are important assets of an organisation and competition for talent has become eminent as managers are hoping to find the appropriate human resources thus DSS such as Employee Recruitment using Multifactor Evaluation Process (MFEP) becomes relevant to assisting the organisation recruit the best employees. In addition, Simrek, Albizri, Jonson, & Archer (2021), explained that hiring of employees has an impact on financial cost and organisation performance. This may have a bearing on achieving the strategic goals of the organisation. The execution of any organisation strategy is complex when decisions are affecting different departments within the organisation that have their own strategies and action plans to deal with this challenge DSS are utilised (Fadda,Perboli,Rosano, Mascolo & Masera,2022). Therefore, this paper critically reviews and analysis the influence of the support systems on strategic management. This paper focuses on the impact of the DSS on.strategic management and meta-analysis
The main objective of the study
The main objective of the study to investigate the impact of decision support systems on strategic management a meta-analysis.
The sub-objectives of the research were to:
- To determine the impact of decision support systems on strategic management a meta-analysis.
Hypothesis
- H0-There’s no relationship between decision support systems on strategic management.
- HA-There’s a relationship between decision support systems on strategic management.
The study research model (Angula)
The research model for this study was represented by the simple linear regression model:
Y = β0 + β1 X1 +β2 X2 + β3 X3; where Y = Firm performance, X1 = decision support systems; X2= strategic management; β = gradient of the slope; β0 = intercept of the graph on the y-axis; Y is
2. Literature Review
Theoretical literature
Decision support systems- define
A Decision Support System (DSS) is a computerised system that assists with diagnostic decision-making to influence quality and practice (Tokgöz et al., 2024). It is a data-driven assessment tool for planning and policy-making in organisations and has been used in various disciplines, such as health care (Tokgöz et al., 2024), renewable energy (Esmat et al., 2023), roads and freight services (Karam & Reinau, 2022), manufacturing (Elmas et al., 2022), corporate sustainability (Kitsios et al., 2020), marketing (Chornous et al., 2022), and accounting (Biswas & Akroyd, 2022), among others. Although computer-based DSS might be difficult due to elements such as technology, organisation, and end users, data science technology is strongly recommended in this technology-driven era to make informed strategic decisions to improve organisations' performance (Chornous et al., 2022).
Strategic management
Having a business strategy that clearly outlines your objectives is critical for any organisation that endures to realise its goals and create a competitive advantage. Sheldon et al. (2024) defines strategic management as an on-going planning, monitoring, analysis and assessment of the resources and processes an organisation should have in place to meet its goals and objectives. Because business environments are dynamic, an organisation must constantly assess its strategies to stay competitive and meet its long term goals, (Shelton et al 2024) . According to Kitsios and Kamariotou (2018) The implementation of DSS is considered crucial to sustain competitive advantage because the business environment is becoming more and more turbulent. The strategic application of DSS tools enables an organisation to have a clear understanding of its mission, its vision and where it wants to be in the future and the values that will guide its actions. This process as reflected by Shelton (2024) requires a commitment to strategic planning, which is a subset of business management which requires an organisation to identify its shortterm and longterm goals through process planning and resources.
The impact of decision support systems on strategicmanagement
In today’s business environment, where successful market positions cannot be sustained overtime (Biloslavo, Edgar, Aydin & Bulut, 2024), the ability to make rational strategic decisions is crucial. As management performs extensive and complex analysis, the need for a system to enhance decision-making process becomes crucial. Consequently, DSS have become a key component of strategic management in modern organisations. The implementation of DSS in strategic management where human intellectual resources are combined with computer capabilities significantly improve the quality of decision-making process (Luckyardi, Rahayu, Adiwibowo&Hurriyati, 2023).
As organisations navigate dynamic and complex business environments, it is crucial to understand how DSS play a role in strategic decision making. Designed to support complex decision-making tasks (Kitsios, Kamariotou, Madas, Fouskas &Manthou, 2020), DSS provides relevant data, models and analytical tools (Curlin, Jakovic& Wittine, 2023), to facilitate a more informed decision -making. By integrating data from various sources, DSS provide a comprehensive view of internal and external environments. This according to Yan, Hong, Warren (2022) allows for accurate trend analysis and forecasting which is a crucial task in strategic management. Furthermore, DSS various tools and applications impact strategic decision-making process through simulations analysis where different scenarios and their potential impacts on organisational outcomes are explored. In this way, managers perform risk assessment and make informed decisions based on relevant and accurate information (Biloslavo et al., 2024; Oger, Lauras, Montreuil &Benaben, 2022) rather than relying on intuition and guesswork.
To achieve strategic alignment, which is the alignment of an organisation’s resources with its strategic goals, DSS tools are also used for SWOT analysis purposes. This allows managers to align organisational strategies with environmental opportunities and threats as well as internal weakness and strengths (Oger et al., 2022). This is particularly important in uncertain and dynamic environments, where organisations must anticipate future trends and adapt accordingly (Biloslavo et al., 2024). As a result, organisations need to develop strategies and contingency plans that mitigate threats and capitalise on opportunities. Therefore, DSS tools enable managers to identify opportunities and potential risks, creating room for addressing uncertainties and capitalise on emerging opportunities proactively (Biloslavo et al., 2024).
Building on the role of DSS in facilitating SWOT analysis, it is also essential to recognise how these systems impact on strategic management through the continuous monitoring of key performance indicators and other metrics which are critical to strategic management. DSS provides real-time insights making it possible to track performance against strategic goals (Yan et al.,2022) and adjust timely to ensure that strategic goals are met.
Moreover, to improve their business practices and achieve competitive advantage, organisations must leverage their own internal and available data (Biloslavo et al., 2024, Alasari& Salameh,2020). This calls for swift and data-driven decisions. By providing accurate and timely information, DSS empower organisations to make faster decisions to be one step ahead of their competitors (Biloslavo et al., 2024). Additionally, to create customer value, organisations need to better understand and meet customers’ needs, and DSS play a crucial role in this process. By providing insights into emerging trends and customer preferences, DSS helps organisations to align their strategies with market demands and to improve customers’ experiences (Aarninkhof-Kamphuis, Voordijk&Dewulf, 2024).
While DSS enhance customer satisfaction and decision-making, effective communication and collaboration among management is equally essential to maximise these systems strategic benefits. By providing a centralised platform for data sharing, DSS enables managers to share perspectives that contribute to better decision-making (Luckyardi et al., 2023). Furthermore, DSS enhance collaboration among management by generating visualisations and reports that clearly convey complex data (Yan et al.,2022), making it easier for different stakeholders to engage with organisational strategic initiatives.
Although DSS positively impact on strategic management, organisations must also consider various challenges to fully leverage the benefits of these systems as strategic management tools. Various scholars (Alojail et al., 2023; Curtin et al., 2023; Luckyardi et al., 2023), reiterated that DSS requires significant investment in time, technology and capacity building. Supporting this is Aarninkhof-Kamphuis et al. (2024) who equally emphasised that DSS effectiveness depends on the design of the system and the quality of the data it generates. Managers must therefore be equipped with the necessary skills to interpret DSS reports to successfully integrate the information into the strategic decision-making process effectively (Curtin et al., 2023; Luckyardi et al., 2023). Therefore, to leverage DSS capabilities effectively, organisation must be prepared to acquire the necessary resources and expertise.
Empirical literature
The study conducted by Sharan et al., (2023) was about examining the relationship between organisational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions. The design/methodology/approach used in the study was the systematic literature review method in three stages to the 76 articles obtained from scopus, Web of science, google scholar and EBSCO databases. The findings of the study revealed the evolution of the role of OL in innovation, performance, knowledge management and technological adoption and showcases a detailed conceptual model relating technology outcomes (technological innovation and capabilities) to OL outcomes (technology absorptive capacity, technological proactivity, as well as information technology [IT] and organization process alignment). This research attempts to guide managers and policymakers to foster an organizational culture conducive to technological adoption and OL. It helps organizations develop strategies for new product development, including strategic alliances and strategic leadership. This review formalizes the linkages between technological absorptive capacity, technological proactivity and IT with technological innovation and capabilities. It identifies research gaps and elucidates future research directions. This review formalizes the linkages between technological absorptive capacity, technological proactivity and IT with technological innovation and capabilities. It identifies research gaps and elucidates future research directions.
The study done by Kempkes et al., (2023) was about how management support systems affect job performance. The study presents a systematic literature review of research examining the effects of management support systems (MSSs) on various facets of job performance. The review is guided by our conceptual framework that aims to facilitate the understanding of the MSS job performance relation by integrating both mechanisms by which MSS effects can occur and variables that specify the form and/or magnitude of this relationship. Through this theoretical lens, we analyze 271 empirical articles published in leading academic journals between 1974 and 2019. Based on the synthesis of the vast body of empirical evidence, we critically reflect on the current state of knowledge and outline fruitful avenues for future research. We find that while especially task performance effects have received much attention in the literature, effects on behaviors going beyond the prescribed tasks of a job, such as employees’ willingness to exert effort, compliance, and knowledge acquisition, are still underrepresented.
The study done by Kitsios, Fortis (2018) was focused on how Decision Support Systems and Strategic Planning, in information Technology influences performance among SMEs. The study examined how Strategic Information Systems Planning (SISP) contributes to a greater extent of profitability in SMEs by suggesting Decision Support Systems (DSS) modes for strategic alignment which included 4 sub-systems: Environmental Analysis Subsystem, Goal-setting Subsystem, Decision Support Subsystem and Strategy Operating Subsystem. The study utilised a field survey which targeted particularly Informations Systems (IS) Executives. 300 IS executives were targeted for the study to which only 55 Executives provided a sufficient response. The study found that Managers need to focus more on implementing Situational Analysis with greater meticulousness, so that they can implement Strategy Conception and Strategy Implementation Planning with greater agility rather than now. The study further acknowledged that DSS had a greater influence on the performance of SME’s hence it was vital for implementing them.
In understanding the study, the researcher felt that the study leaned more towards how DSS tools can influence the performance of SME’s without actually acknowledging the performance of SME’s without DSS, however the conclusions of the study remains vital in assisting the researcher to achieve the objectives of the study.
The study done by Bader; A. Alyoubi (2015) focused on the application of Decision support systems through the utilisation of knowledge-based strategic management. The study relied on the presentation of empirical literature on the historic development of Decision Support Systems (DSS) and the applications of the concept of Knowledge Management (KM) by looking at how the combined concepts are applied in the field of strategic management.
Throughout this study, the emphasis was shifted more towards the models of development and problem analysis by emphasising that the inclusion of knowledge management and its principles truly enables DSS to provide support for semi- and ill-structured problems.
Secondary data literature review was relied on to analyse the historic development of Decision Support Systems (DSS) and the applications of the concept of Knowledge Management (KM) and also understand the critical role that DSS and knowledge management has and how it can be utilised as a strategic management tool. The study found that Knowledge is a key factor for an organisation towards building a sustainable and successful strategy. Having the right information, together with the knowledge of using it the appropriate way is one of the keys to success. This means that the concept of KM is as crucial to strategic management as any other activity. In understanding the study clearly, the researcher felt that the study focused more on the role of DSS as a knowledge management tool at strategic level, but other areas of DSS as a critical tool at technical and operational levels could still be explored, however this work remains very much relevant in helping the researcher to fulfil the objectives of the study.
Another notable study on the application of DSS in emerging economies is by Modgila, Guptah and Bharat (2020) , which aimed to improve the achievements of the United Nations’ sustainable development goals (SGDs) for a living economy. Using semi-structured interviews with key people, the researchers developed a stakeholders’ framework using a modern information decision support system to make decisions on a mutual basis. The main findings indicated that DSS is essential in making decisions such as allocation of resources, contributions towards development of sustainable communities and economic growth, making it possible to achieve SGDs for living economy. The study further demonstrated the potential of DSS to enhance a shared vision and trust among stakeholders. Through an information decision support system, this partnership can make fast and responsible decisions collectively.
Goncalves, Concalves and Campante (2023) conducted a study on the use of business intelligence tools in the decision-making process in organisations, particularly focusing on sales marketing. The study’s objective was to explore how business intelligence tools could be utilised to analyse and visualise data to make appropriate decisions that can support the decision-making process in the sales area. Concalves et al. (2023) utilised a Vercellis methodology to develop key performance indicators for organisational performance. The study’s findings revealed that DSS users were able to optimise transformation of data from various sources stored in data warehouse to carry out concise, quick and easy-to-interpret analyses. This work underscores the importance of DSS to a better understanding of data-integrated dashboard visualisations for the decision-making process.
- In the health sector, Alojail, Alturki and Khan (2023) investigated the application of the informed decision support framework (IDSF) to ensure the accuracy of decisions and to improve the process that governs and controls decision outcomes. The study aimed to propose a decision support framework from a strategic perspective that could integrate structured, semi-structured and unstructured decisions to improve decision making process in the health sector. Structured decisions are those that are routine and repetitive in nature undertaken by following a definite procedure, while semi-structured decisions require a mix of clear-cut answers provided by accepted procedures as well as judgment. Unstructured decisions involve evaluation, judgment and insights into the problem definitions. Using a multi-faced approach, the study collected data through an intensive literature review and interviews with decision makers. The main findings showed a lack of DSS implemented by organisations to allow informed decision-making. As a result, an IDSF was developed, outlining a comprehensive process for making strategic decisions in organisations.
DDS are influenced by various factors, including human capabilities. A study by Bakri, Ganesan, Amran, Ashaari and Nazri (2019) emphasised the importance of leaders’ experiences in realising the full potential of DDS. The authors investigated the influences of leaders’ sustainability experiences on procurement practices using decision support systems. By utilising the resource-based theory, the leaders are regarded as internal resources responsible for formulating organisational strategy. Hence, they are required to possess skills and knowledge to engage in data analytics for decision making process. Understanding these aspects is essential for leveraging DSS to enhance decision-making process and achieve organisational competitiveness.
Simrek, Albizri,Jonson, & Archer (2021) employed a CAM framework in their study to assist in decision making of hiring the baseball players at Major League Baseball (MLB) due to financial costs and the long term impact it has on organisational performance. A design science research paradigm and the Cognitive Analytics Management (CAM) theory based on the Kernel theory to develop the research framework and the analytical modelling to personnel decision making. Based on the CAM the study proposed a tool to facilitate the implementation and develop it using the R programming a web-based decision support tool and incorporated the best performing model Random Forest. However, it did not point how its predictive analytics and machine learning techniques assisted on the impact it has on financial costs and organisational performance and how the tool can be implemented or adopted to realise the strategic management a meta-analysis.
Another study conducted on the Decision Support System (DSS) for Employee Recruitment using Multifactor Evaluation Process (MFEP) by Verina, Fauzi, Nsasari, Tanjung &Iriani (2018), identified the need to use the MFEP quantitative method to support in decision making on recruitment of the best employees at a university, instead of using the usual process of looking at psycho test results, interviews and computer user skills criterions without a system it makes it difficult to make a decision to select the best candidates from the applicants. In addition, there is a need for the system that can assist in giving subjective and objective factors important for recruitment and selection of employees using the existing criteriasnamely;psycho test results, interviews and computer user skills.Verina et al. (2018), explained that the decision support system have assist with recruitment decision making. The study acknowledges the relevance of using the decision support system for recruitment and selection using the MFEP method using the criterion to select the best candidate. However, the criterions in the MFEP are too limiting, thus it is recommended to consider applying more than one DSS to do a comparison to find the best DSS that may support the recruitment evaluation process for efficiency and effectiveness in decision-making.
DSS has been utilised in healthcare for medical diagnostics and patient care. Tokgöz et al. (2024) used the Human-Organisation-Technology Fit (HOT) model in German hospitals to investigate the implementation factors and adoption of AI-based DSS, with an emphasis on strategic and governance efficacy in adopting AI-driven DSS to improve patient care. In particular, the study examines hospital management's perspectives towards AI-based DSS deployment. Although the study agrees that human, organisational, and technological factors influence DSS adoption, there are limitations to this model because broader social and institutional factors (managerial buy-in, trust, openness and teams, and willingness to accept technology) that influence DSS adoption were rarely considered. As a result, there is a need for a comprehensive research methodology that goes beyond the HOT-fit model and investigates socio-technical aspects that influence DSS adoption. Although Tokgöz et al.'s (2024) quantitative study is significant, the role of people as enablers of DSS adoption was not thoroughly investigated. Narratives cannot be adequately measured using descriptive statistics; hence, a qualitative methodology would be appropriate given the studied attitude variable. Furthermore, given the complexities of their employment arrangements, managers' motivations and dispositions may be biased. As a result, non-managerial staff should be included in the sample because they are directly involved with DSS problems.
DSS was also utilised in recruitment and personnel reassignment decisions. Hajnic and Boshkoska (2021) used a web-based DSS to solve three human resource management challenges: (1) selecting the most suitable employees for transfer to an organisation unit (OU) without hiring new candidates, (2) allowing employees to be exchanged between OUs based on employee skills and OU requirements, and (3) preventing employees from overlapping in OUs for a previously defined period. Despite using real-time corporate data, the DSS implemented had a beneficial impact by reducing the lead time necessary for shifting an employee from one business unit to another by 83% and operational costs by 88%. Although the concept demonstrated success, it was found to be restricted because employee transfers are impacted by operational demands of the organisational unit rather than HR functional needs such as career progression, job rotation, succession planning, and leadership courses. Furthermore, despite its strategic ingenuity in preventing unethical acts such as corruption and undesirable commercial relationships, the choice to approve or reject an employee for transfer remains a managerial prerogative, not the responsibility of the HR department. As a result, institutional elements such as power dynamics, marginalisation, and favouritism may emerge.
Simsek et al. (2021) investigated how web-based DDS may be utilised in strategic sports management using the Cognitive Analytic Management model (CAM), an objective-driven paradigm that uses data to make informed decisions to transform an organisation and achieve its goals. This analytic quantitative methodology allows for a strategic decision to extend or not renew a player's contract based on age, win above replacement, and player sourcing and acquisition. However, the shortcomings of the web-based DDS are (1) bias in data interpretation considering the human factor in applying decisions in real life; (2) although users of the system require no expertise in statistics, machine learning, or artificial intelligence, one still needs to have a background in these specialisations to decipher a decision because not comprehending these areas renders the system susceptible to human manipulation.
Theoretical framework
Studies on DSS commonly draw on resource-based view theory, contingent theory (Bakri, Ganesan, Amran, Ashaari& Nazri, 2020) and relational view theory (Guptai, Modgil, Bhattacharyya & Bose, 2022:228). The resource-based view (RBV) explains the efficient utilization of organizations’ diverse resources to develop their capabilities (Guptai et al., 2022:228). In this regard, organizations must utilize its available resources and adopt appropriate decision-making tools to compete effectively. While for contingent theory, given the environmental uncertainty, there is no correct way to make decisions in organizations (Guptai et al., 2022:228). This implies that to make an optimal decision, different approaches can be adopted while being cognisant of internal and external factors present at that time. The relational view theory is based on the premise that organizations opt to strengthen and mobilize their extremal resources when they are unable to compete in the global arena with their own capabilities and resources (Gupta et al., 2020:228).
This study theoretically developed the relationship between DSS and strategic management by drawing on the organizational capability theory (OCT) which is essential to examine an organization's capacity to combine its resources and processes to achieve strategic objectives (Lin, Li, Luo & Benitez, 2020:2). OCT is based on the assumption that to solve organizational problems, the ability to integrate, build and reconfigure one’s resources base is fundamental (Laguir,Gupta, Bose, Stekelorum&Laguir, 2022:5) for thriving in dynamic environments. Of particular importance is management capabilities and technology-related skills essential for the decision-making process (Bharadiya, 2023:25). Therefore, an organization's decision-making capabilities through DSS is essential for sustainable competitive advantage.
The OCT is tailored to the theoretical arguments in this review paper for two purposes. First, it acknowledges the potential of DSS to only make better decisions, but also increases organizational capabilities. Secondly, aligning DSS with an organization’s strategic objectives ensures that organizations do not only utilize DSS as operational tools but as strategic assets essential to navigate environmental uncertainty. As such drawing on the premise of OCT provides a sensible explanation of examining the linkage between DSS and strategic management.
Conceptual framework
The relationship between the independent and dependent variables is explained by the conceptual framework shown in Figure 1 below. The following are the ways in which decision support systems affect strategic management and how they are mutually reinforcing or interdependent: The plans to upgrade Namibia's strategic management decision support systems would boost organizational performance and give the company and its staff a clear direction.
Figure 1: Conceptual framework on the impact of decision support systems on strategic management
Source: Researcher’s own conceptualisation
3. Research Methodology - Materials and Methods
The study conducted a literature review analysis of the existing research on the use of impact of Decision Support Systems on Strategic Management and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed for conducting this systematic review.
4. Empirical Results
Summary of findings
The study sought to test the following hypotheses:
The table below provides a summary of the findings of the literature review.
| No | Author / Date | Hypothesis | Finding | Decision Accept / Reject |
| 1 | H1 | The present study found that that there is no relationship between organisational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions.
|
Accept | |
| 2 | H2 | The present study found that the impacts of management support systems affect job performance. | Accept | |
| 3 | H3 | The present study found that Decision Support Systems and Strategic Planning in information Technology influences performance among SMEs.
|
Reject |
Table 1 Summary of findings
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Introduction
This section revisits the stages taken in carrying out this research. The purpose of the study was to examine the impact of decision support systems on strategic managementthrough the use of existing literature related to the impact of decision support systems on strategic management.
Summary of major findings
From the presented meta-analysis secondary data it was revealed that there is no relationship between aorganisational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions. The findings of the study revealed the evolution of the role of OL in innovation, performance, knowledge management and technological adoption and showcases a detailed conceptual model relating technology outcomes (technological innovation and capabilities) to OL outcomes (technology absorptive capacity, technological proactivity, as well as information technology [IT] and organization process alignment). This research attempts to guide managers and policymakers to foster an organizational culture conducive to technological adoption and OL. The study examined how Strategic Information Systems Planning (SISP) contributes to a greater extent of profitability in SMEs by suggesting Decision Support Systems (DSS) modes for strategic alignment which included 4 sub-systems: Environmental Analysis Subsystem, Goal-setting Subsystem, Decision Support Subsystem and Strategy Operating Subsystem. The study utilised a field survey which targeted particularly Informations Systems (IS) Executives. 300 IS executives were targeted for the study to which only 55 Executives provided sufficient response. The study found that Managers need to focus more on implementing Situational Analysis with greater meticulousness, so that they can implement Strategy Conception and Strategy Implementation Planning with greater agility rather than now. The study further acknowledged that DSS had a greater influence on the performance of SME’s hence it was vital for implementing them. The study found that Managers need to focus more on implementing Situational Analysis with greater meticulousness, so that they can implement Strategy Conception and Strategy Implementation Planning with greater agility rather than now. The study further acknowledged that DSS had a greater influence on the performance of SME’s hence it was vital for implementing them.
5. Discussion
From the presented meta-analysis secondary data it was revealed that there is no relationship between aorganisational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions. The findings of the study revealed the evolution of the role of OL in innovation, performance, knowledge management and technological adoption and showcases a detailed conceptual model relating technology outcomes (technological innovation and capabilities) to OL outcomes (technology absorptive capacity, technological proactivity, as well as information technology [IT] and organization process alignment). This research attempts to guide managers and policymakers to foster an organizational culture conducive to technological adoption and OL. The study examined how Strategic Information Systems Planning (SISP) contributes to a greater extent of profitability in SMEs by suggesting Decision Support Systems (DSS) modes for strategic alignment which included 4 sub-systems: Environmental Analysis Subsystem, Goal-setting Subsystem, Decision Support Subsystem and Strategy Operating Subsystem. .
6. Conclusion
The researcher came up with some recommendations which are herewith being directed to the management of public enterprises in Namibia.
- In the agricultural sector, DSS can optimize land use, resource management, and market strategies. Recommend developing tailored DSS tools to address challenges such as climate variability and market access.
- Tourism: For the tourism sector, DSS can enhance visitor management, marketing strategies, and resource allocation. Suggest integrating geographic information systems (GIS) to better understand tourist patterns and preferences.
- Mining and Resources: Given Namibia’s significant mining industry, DSS can improve resource planning, safety management, and operational efficiency. Recommend focusing on systems that handle large datasets and predictive analytics.
Future researchers are advised by the study to consider all factors that were not addressed in this investigation. Furthermore, everything that was left out of this study ought to be considered in further investigations. The relationship between strategic management and the effects of decision support systems.
Acknowledgments: In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).
References - The references follow APA style of reporting.
CrossRef is instead of https://doi.org/10.3390/xxxxx
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