Purpose: The purpose of this paper is to propose a quantitative methodology for the identification of the causal relationships between strategic objectives in a strategy map of a balanced scorecard. This is done to face the possible weaknesses described in the literature regarding the causal links and the difficulty in validating the relationships. Design/methodology/approach: The proposed method combines the multi-criteria decision-making method called decision-making trial and evaluation laboratory (DEMATEL) and an optimization model. DEMATEL is used to establish the importance of the strategic relations between strategic objectives, and the optimization model is used to find the relations that are more “important” and should be included in the strategy map. The method was created by reviewing the existing literature, modeling the problem, and applying it in a company. Findings: The most important results of applying this methodological design include that the proposed method maintains the BSC classical structure; it also enables the generation of several alternatives to support the decision-making process in terms of strategic objectives for a better organizational performance. Practical implications: The method facilitates the decision-making process by presenting several alternatives of strategy maps according to different levels of organizational criteria. In fact, these alternatives help the organization in focusing on the most important aspects of the strategy map. Consequently, managers may identify where to pay more attention and resources in order to achieve the most important objectives of the company. Hence, this method, as a support for decision makers, enables (and requires) the active participation of senior managers and any kind of decision makers in creating and valuating objectives, relations, constraints, importance, and parameters of the optimization model. Originality/value: DEMATEL has been used to design strategy maps. The contribution of the paper is the use of a linear programming model to select those relationships that should be included in the strategy map. It allows manager to focus on those strategic elements that are important from a strategic point of view. The application in a company showed that the contribution is not only theoretical but practical as well.

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.