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This paper examines the dynamics of structural transformation in Morocco since 1970 by analyzing input-output tables expressed in terms of employment and output levels across 24 sectors.

This study employs a twofold methodological approach. Firstly, it examines the evolution of sectoral employment shares over time using World Bank data. Secondly, it utilizes Input-Output analysis to examine structural shifts in Morocco's economy, focusing on sector-specific output and employment data. The primary data source is the Eora Global Supply Chain Database, covering the years 1970, 1980, 1990, 2000, and 2015. Additionally, to transition from production-based to employment-based input-output tables, the study leverages employment and output data from the Penn World Tables to calculate the diagonal labor coefficient matrix.

First, our analysis reveals that Morocco's economic transformation has been slower compared to high-income countries. Structural changes, as evidenced by the evolution of employment shares by sector, show a gradual decline in agricultural employment share over the period 1991-2019, accompanied by a shift towards the services sector. This shift, driven by favorable conditions in the services sector and increased capital use in agriculture, has resulted in premature deindustrialization. The industrial sector's employment share has remained stable due to its capital-intensive nature. Second, Input-Output analysis reveals a pronounced premature tertiarization of the Moroccan economy. Between 1990 and 2000, the tertiary sector saw a dramatic rise in both backward (167%) and forward (68%) linkages, while the primary sector's backward linkages fell by 33% during the same period. Although the primary sector’s linkages increased by 10% from 2000 to 2015, the secondary sector experienced a consistent decline in backward linkages, dropping 12% from 1990 to 2000 and an additional 10% from 2000 to 2015. Employment linkage analysis further underscores this shift, with a 12% increase in the tertiary sector’s backward linkages from 1990 to 2000, contrasted by significant declines in the primary (51%) and secondary (7%) sectors. These trends highlight an unsustainable move towards services without concurrent industrial development, challenging balanced economic development.

As it is unanimous, the structural transformation of Morocco remains relatively slow and characterized by a shift of the labor factor from the primary sector to the tertiary sector, with a limited job creation by the secondary sector considered as the pillar of any structural transformation. This paper advances the field of research on structural transformation by elucidating the premature tertiarization of the Moroccan economy and the slowness pace at which the transformation of its economic fabric is occurring, thereby filling the empirical gap.

The paper is devoted to modeling a pricing policy of competitive firms in a “closed” economy framework.

The proposed model can be regarded as an analog to CGE model and is based on the intersectoral balance methodology incorporating linear demand functions for goods and services.

By performing different model experiments, we show that a certain degree of competition can bring more profit to all competing firms, than in case of complete absence of such competition, what is also supported by empirical investigation. This finding implies that monopolies may perform worse than competitive firms, what contradicts with the modern provisions of economic theory, stating that monopoly is the most lucrative type of market structure for a producer. The discovered effect occurs due to the aggressive pricing policy, adopted by monopolies, spurring up the inflation spiral, which is most obvious if monopolies are strongly interdependent in terms of production matrix. This inflation spiral drives prices too high, what negatively reflects on firms’ costs and, consequently, results in monopolies receiving less profit.

The proposed model can also be useful for understanding and assessing various economic consequences after different external or internal shocks, what is especially crucial when conducting monetary or fiscal policy.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

The study attempts to examine the impact of the US-China trade war on Vietnamese exports to the United States, which has consistently served as a key market for Vietnamese goods and services in recent decades. The heterogeneous effects of the trade war on different export sectors are also evaluated.

The secondary data on Vietnamese exports to the US at a 6-digit level is collected from UN Comtrade. Besides, the difference-in-differences (DiD) method is employed to analyze the impact of the trade war on exports from Vietnam to the United States.

The findings revealed a 14% increase in total Vietnamese exports to the United States due to the trade war. Examining heterogeneous effects, certain industries, such as plastics, iron or steel articles, textiles and garments, and machinery and mechanical appliances, experience significant benefits. However, the study did not identify statistically significant effects on other sectors, such as electrical machinery products, agricultural and forestry, and furniture.

The paper is one among limited studies considering the causal effects of the trade war on a developing country, accounting for the heterogeneous effects on different export sectors.

The geographical location of Yazd province has significant potential for religious tourism. To make the most of this opportunity, it is important to develop an interpretive structural modeling. This paper aims to outline a model for the development of religious tourism in Yazd province as a Global Religious Destination by identifying and analyzing the interaction of effective drivers in future religious tourism development.

The research methodology is based on the interpretative paradigm and is developmental in nature. It takes an exploratory-analytical approach through the adoption of an inductive method and uses mixed research (qualitative/quantitative) involving religious tourism experts and tourists. The study consists of three main steps. In the first step, effective drivers in future religious tourism development were identified through content analysis of published articles. In the second step, the identified drivers were finalized through a survey of experts. Using futures research and Micmac software, a model was designed to investigate the interaction of the future drivers of religious tourism development. In the third step of the research, 384 tourists who visited Yazd Global Religious Destination participated to check the accuracy of the presented model.

Content analysis and review of scientific documents have identified 14 effective drivers in future religious tourism development that have finalized in a layered model. The model identified factors from the most influential at level one to the least influential at level eight. the theoretical foundations of this research confirmed by 384 visitors participated.

developing religious tourism, whether in the form of a religious city, a religious value system, a religious ceremony and a religious business, requires a comprehensive view that includes tourist’s mental and visual imagery, destination brand’s ideals and visions, religious belief, governmental and formal activities and the material and spiritual capital that can offer religious life style in the world markets to audiences from all over the world in the best way and in the dynamic conditions of competition between destination brands, occupy the first rungs of the audience’s mental ladder.

This paper primarily aims to identify the multilevel learning processes emerging from abrupt telework implementation in a public knowledge-intensive organization (KIO) amid the COVID-19 crisis.

This single-case process research was guided by interpretivist epistemology. Empirical data from documentary research and 41 interviewed managers were processed by inductive qualitative analysis using the multilevel learning theoretical model.

Eight types and three modes of learning processes during the COVID-19 pandemic were identified in a public KIO, iteratively emerging in multilevel learning dynamics during the compulsory adoption of telework and replacing the face-to-face work mode conducted since its foundation.

As insider researchers, while daily and privileged access to the field was obtained, it also demanded their continuous effort to maintain transparency and scientific distancing; conceptual results are restricted to process theorisation studies, specifically the 4Is theoretical model in the scope of crisis learning process studies concerning KIOs.

This study provides evidence for managers to adopt interactive dynamics among eight multilevel types and three learning modes of emergent learning, developed during the COVID-19 pandemic, and support learning practices’ implementation and routinisation across three organizational levels in crisis situations. In addition, evidencing emergent types of learning enables organizational learning (OL) researchers to examine how organizational structures and work practices either promote or inhibit different learning types and impact multilevel learning when adopting teleworking during a crisis.

This research has theoretical value in two ways: (i) Providing empirically supported knowledge: This involves understanding multilevel learning processes resulting from emergent learning in a public KIO that abruptly adopted teleworking during a crisis context; (ii) deepening process theorization studies on OL: To achieve this, we enhance the 4I model by incorporating eight types and two modes of learning processes. These processes iteratively emerge from the individual and group levels towards the institutional level in a public KIO.

The purpose of this paper is to approach predictive maintenance (PdM) of brushless direct current (BLDC) motors using audio signal processing and extracting statistical and spectral features to train classical machine learning (ML) models.

The proposed methodology relies on classification predictive model that shows the motors prone to failure. To verify this, the model was implemented and tested with audio data. The trained models are then deployed to an Industrial Internet of Things (IIoT) application built using Django.

The implementation of the methodology allows for achieving performance as high as 92% accuracy, proving that spectral features should be considered when training ML models for PdM.

The proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for BLDC motors.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.