The Utilization of Artificial Neural Networks

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This entry is adapted from the peer-reviewed paper 10.3390/admsci12020063

Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget.

change management Balanced Scorecard Artificial Neural Networks project performance

1. AI in Decision-Making

The concepts of AI and AI systems were put forward to the scientific community during the 1950’s. Recent technological advancements such as the boosting of computing power and data processing speed contributed to the rapid development of AI systems (Duan et al. 2019); therefore, with all this technological progress, the field of Big Data emerged. Big Data are so intricate that conventional statistical methods cannot be applied in an effort to analyze them (Di Vaio et al. 2022). On the contrary, AI techniques could be exploited for Big Data analysis in order to obtain the maximum advantage of the available information.

AI is commonly defined as “the ability of a digital computer or computer-controlled robots to perform tasks commonly associated with intelligent beings such as the ability to reason, discover meaning, generalize, or learn from past experience” (Copeland 2021). The element which differentiates AI systems from the rest of computer systems is to self-learn from the input data. Consequently, these systems do not merely process data, but they update their decisions whenever new inputs are received (Vincent 2021); therefore, the term AI emerged because these applications resemble the function of the human brain.

The utilization of AI in the decision-making process is one of the major breakthroughs in Computer and Management Science. Bader et al. (1988) categorized six roles in AI’s contribution during this process: assistant, critic, second opinion, expert consultant, tutor, and automaton. Decision-making is reinforced by AI, whereas with the use of predictive analytics, one can timely identify the upcoming changes and receive the appropriate corrective action. Additionally, the time required for the analysis of big datasets is diminished and the possibility of human error is reduced (Valle-Cruz et al. 2021); however, in order to make user results tangible, considering the application of AI systems in the decision-making process, upper management should incorporate them into their organizational strategy (Di Vaio et al. 2022).

It should be noted that AI systems cannot proceed to decision-making unless predefined instructions have been provided by the user (Di Vaio et al. 2022). Unlike human effort, the decisions of AI systems should not be influenced by prejudgments or emotions. Nevertheless, an AI system can extract decisions by merely taking into account the information which has been entered into it. Hence, there is a collateral bias in this process because AI cannot take into account information that exists outside its system (Vincent 2021). Moreover, the ambiguity of the way in which AI produces outputs could result in a lack of trust and transparency (Valle-Cruz et al. 2021). Humans tend to be reluctant when they do not fully comprehend the way in which an algorithm comes to a decision and are not truly involved in understanding how AI systems operate before they completely integrate them into organizational processes (Jarrahi 2018). AI is highly efficient when there is enough available data from the past to foresee future actions (Tambe et al. 2019); however, in certain cases, there is a lack of historical data, and their potential to arrive at precise decisions is still under examination (Vincent 2021).

Despite the aforementioned concerns regarding the use of AI in decision-making, several research efforts have shown the beneficial results of the collaboration between humans and AI. These works have highlighted the numerous applications related to the utilization of AI techniques, as well as the particular issues concerning their contribution in assisting or replacing humans in several processes. For instance, a recent experiment at Yale University indicated that smart bots, in the context of an online game, assisted human teams to enhance their performance by minimizing the medium time needed to solve a problem by 55.6% (Shirado and Christakis 2017). Other researchers have looked into the application of AI in making weather forecasting predictions and the possibility of replacing traditional weather models (Schultz et al. 2020). The current evolution brought about by this technology assists decision-makers in comparing the possible effects and risks related to decision alternatives (Vincent 2021). Moreover, in the medical field, numerous sophisticated applications of AI have been recently developed. Wang et al. (2016) presented an algorithm which had the ability to detect metastatic cancer. Their algorithm had 92.5% success rate, whereas an unassisted pathologist performing the same task had a success rate of 96.6%. When algorithm predictions were combined with human diagnoses, the pathologist’s success rate improved to 99.5%, thus the human error rate was decreased by 85%.

Organizations that have integrated AI into their organizational strategy have seen significant benefits. Manzoor (2016) highlighted that advanced analytics techniques are utilized by top-class organizations five times more regularly than low performing ones. With the accelerating progress of these technologies, AI could be integrated in increasingly more complicated functions which require intellectual capacity. For instance, in the field of financial advising and wealth management, companies have recently been using AI and have remodeled their operations by providing investors with cutting-edge technology named robo-advisors (Méndez-Suárez et al. 2019). These algorithms utilize AI to mechanically allocate, manage, and improve clients’ assets for short- or long-term investments (D’Acunto et al. 2019).

The application of AI in government practices could create value in multiple functional areas such as e-government, healthcare, transportation, energy management, and defense. The European Parliament included AI as one of the leading technologies which will facilitate the strategic objective of digitization during this century (Sobrino-García 2021). A big challenge that policy makers have to address is how society can take advantage of emerging technologies without affecting individual freedom and privacy (Anagnostopoulos et al. 2021). To this end, the European Commission made the first worldwide attempt to regulate AI with the “Artificial Intelligence Act”. This draft regulation attempts to set out horizontal rules for the utilization of AI-driven products, services, and systems across the EU market (Kop 2021).

A wide variety of studies have proven the positive impact of AI utilization in public administration. Al-Mushayt (2019) suggested an AI solution which improves e-government services, whereas Wirtz et al. (2019) proposed a comprehensive framework which represents the crucial aspects of AI in public administration. Furthermore, the analysis of large datasets can assist authorities in detecting tax frauds (Baghdasaryan et al. 2022). In their research, Al Nuaimi et al. (2015) analyzed the key applications that can be implemented in the development of smart cities. Anagnostopoulos et al. (2017) utilized smart devices in waste management and Moustris et al. (2020) developed a forecasting model based on ANNs to predict the energy demand of Tilos Island in Greece, respectively. Additionally, the thorough research of Valle-Cruz et al. (2021) proposed an approach based on an ANN which assists public spending allocation to increase Gross Domestic Product, decrease inflation, and reduce income inequality.

Apparently, in many fields of management, prediction problems emerge. The solution lies in determining the relationship between two or more variables. The conventional methods used to solve these problems rely on regression techniques; however, if the relationship among the variables is too complex, traditional techniques are bound to fail. It has been proven that problems with many complicated attributes can be solved more efficiently by ANNs (Kosa 2013).

In their previous research effort, Psarras et al. (2020) utilized predictive analytics to assess the extent to which the financial measures of companies that participated in a funding program were influenced. This process affected the financial perspective of the government body which implemented the specific program. In the present study, the effects of non-financial measures are examined by utilizing an ANN. In sum, this research effort aims at predicting how the funding program being studied influenced the non-financial measures of the government body that implemented it.

2. The Association of the Balanced Scorecard with Performance and Change Management

The utilization of performance measurement data is facilitated by recent technological advancements. In business environments, performance data are used to make the most important decisions, such as the formulation of a strategy, the design of a service, or the implementation of change. More specifically, in the public sector, measuring performance forms an obligation to publicly report program results. Performance management emerged as a concept in the context of New Public Management (NPM), which is a managerial approach that revolutionized public administration in the early 1990s (McDavid et al. 2019). Its foundations lie in the importance of clarifying program objectives, measuring and reporting results, as well as holding all the involved parties responsible for achieving the expected outcomes (Hood 1991). NPM emphasizes associating financing with targeted outcomes. Psarras et al. (2020) investigated how the actions of a funding program affected the financial indicators of the companies which received financing. In the current study, the scope of the previous research effort will be further extended. Additional data are utilized and investigated, by means of ANN as well as through the impact of financing on the rest of the perspectives of the BSC.

In some instances, performance measures can effectively be integrated into evaluations. Program evaluation has been transformed by the aforementioned concept which relates to public administration. It is a process which exploits information systems to minimize the uncertainty levels for decision makers (McDavid et al. 2019). The evaluation outcomes and performance management systems play a crucial role as to how managers deal with their programs (Hunter and Nielsen 2013). Performance measurement and reporting may sometimes lead to numerous implications concerning program alterations or the revision of strategic objectives. By means of this process, the expected outcomes of a specific program are measured and its progress is monitored over time. In cases where deviations from the initial objectives are detected, certain corrective actions can be considered, such as the reallocation of funding.

The initial performance measurement models placed more emphasis on monitoring the financial indicators of an organization. Given the fact that business environments have been changing rapidly, and have become increasingly complex, a more balanced and integrated approach was needed to evaluate performance more holistically (Van Looy and Shafagatova 2016); therefore, organizations were compelled to go beyond conventional performance measures and develop operational measures whose performance cannot be assessed through financial indices (Papalexandris et al. 2005). Kaplan and Norton (^[1] 1996) conceptualized a management system that would integrate both traditional quantitative and qualitative performance measures, which aided the development of the BSC.

The BSC constitutes one of the most significant business tools developed over the last few years and is widespread in various fields (Grigoroudis et al. 2012). It investigates the organization by means of four different perspectives: (1) financial, (2) customer, (3) internal business process, and (4) learning and innovation. The BSC helps translate strategy into operational performance measures and connects the organizational targets with those of its relevant departments. It is a tool that supports strategic planning and change management by diffusing the strategy and vision of the organization to each and every employee (Kaplan and Norton 1996). Additionally, the BSC facilitates change management as the goals of this process are linked to specific actions and timetables (Salmon et al. 2019). Finally, the implementation of the action plan is measured through the indices which have been assigned to each and every aforementioned perspective.

Many research efforts have utilized the BSC to measure the performance of various schemes. Greatbanks and Tapp (2007) investigated the impact of applying the BSC in a pubic organization. Their findings suggest that the employees who have a better understanding of their role, prompt the enhanced fulfillment of the organizational strategy. Northcott and Taulapapa (2012) indicated that the BSC is perceived by public organizations as a performance measurement tool, although its performance management role remains underutilized. Additionally, Rompho (2020) applied the BSC to measure school performance and discovered that there is a cause-and-effect relationship between the three viewpoints (customers/students, internal processes, learning and innovation). In any case, they did not unveil a relationship between the three previously mentioned perspectives and the financial perspective. Elbanna et al. (2015) applied 33 indicators in order to measure hotel performance by means of the BSC. Additionally, Gambelli et al. (2021) studied the performance of small ruminant farms in seven European countries. Their findings indicate that not much emphasis is placed on innovation issues, which may give an explanation for the low performance and longstanding downturn of this sector.

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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Alkinoos Psarras

Theodoros Anagnostopoulos

Ioannis Salmon

Yannis Psaromiligkos

Lazaros Vryzidis

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1. AI in Decision-Making

2. The Association of the Balanced Scorecard with Performance and Change Management

References