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Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

The dynamic environment has necessitated searching for new ways for managing and grooming people for better performance. The purpose of this study is to explore ambidexterity in human resource management (HRM) for better management of paradoxical tensions and its effect on employee performance. Further, this research also addresses the black box in this relationship by evaluating the extraneous (managers’ ambidextrous orientation) and mediating (individual ambidexterity) variables in this relationship.

A quantitative research methodology has been used to explore the ambidexterity in HRM and its impact on employee performance. Around 470 banks have been contacted for data collection. The data have been thoroughly examined for reliability and validity. Further, it has also been checked for common method variance.

The findings revealed that individual ambidexterity mediates the relationship between ambidextrous HRM and employee performance. Further, managers’ ambidextrous orientation moderates the relationship between ambidextrous HRM and individual ambidexterity.

The present study makes an important contribution to the strategic HRM literature in general. The theoretical and practical implications have also been put forth for academic and practical fields. Lastly, the study contributes towards ambidexterity literature by examining it from an HRM perspective.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.

The authors investigated whether remote learning and its associated factors affect students’ adoption of Zoom, Microsoft Teams, Blue Jeans and other conference applications.

The study used a quantitative design; data were collected by surveying B-school students in Hyderabad using a questionnaire prepared adopting the validated scales. About 33 items were used to measure nine reflective constructs: remote learning, performance expectancy, adoption behavioral intention, institutional support, ecological acceptance, habit formation, hedonic motivation, attitude towards conference apps and social influence. The exploratory and confirmatory factor analyses were carried out, and hypotheses were tested using IBM SPSS and AMOS version 28.

A 61% variance in students’ adoption behavioral intentions and a 37% variance in students’ attitude towards conference apps are accounted for by remote learning, performance expectancy, institutional support, ecological acceptance, habit formation, hedonic motivation and social influence. The exogenous constructs of institutional support, environmental acceptance, habit formation and social influence are statistically significant and influence students’ adoption and behavioral intentions toward conference applications. The attitude towards conference apps fully mediated the relationship between students’ adoption behavioral intentions and performance expectancy. However, the constructs of environmental concern, social influence and habit formation are partially mediated. This study provides empirical evidence that attitude towards conference apps, environmental acceptance, performance expectancy, institutional support, habit formation and social influence are the key predictors of remote learning and students’ adoption of and conference applications.

This study was limited to the B-schools of Hyderabad city, an Indian metro. To encourage students to adopt remote learning through conference apps, academicians should appropriately illustrate the idea of remote learning. To enable students to learn while on the go, educational institutions should offer intuitive applications with enhanced reading layouts. Second, since internet access is required for remote learning, this study is crucial for service providers. To make it simpler to obtain educational resources, the internet should be more widely accessible. Third, since technology is linked to remote learning, this type of study is essential for the education sector since devices need to be developed.

The pandemic has caused restructuring of the educational system, necessitating new strategies for distance and virtual learning for teachers. In the future, teachers will adopt techniques centered around the use of virtual platforms, social media and video production. The government should establish sufficient infrastructure to facilitate online education and assist instructors in becoming more knowledgeable and proficient in the use of technology, especially when creating, executing and assessing online instruction.

The purpose of this study is to determine how beneficial it is to use online/remote learning with Zoom, BlueJeans, Microsoft Teams and other conference software in particular. Both the online/remote learning method itself and the learners' capacities and capabilities for adjusting to new normal scenarios should be developed in educational environments.

This research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience.

Text mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint.

The study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations.

The dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement.

In e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development.

Drawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.

The purpose of this paper is to identify the causal (not correlational) effect of women’s entrepreneurship on corporate social responsibility (CSR) practices in Latin America.

This study builds on a hitherto unexploited sparse data set on Latin American B Corporations to identify the causal relationship of interest and on a (synthetic) instrumental variable method.

The results confirm that women’s entrepreneurship has a positive causal effect on social responsibility. This study finds that an increase of 1% in the proportion of women entrepreneurs leads to an increase of 0.5 in the B Impact Assessment score, the CSR indicator.

This study contributes to the literature by providing robust statistical evidence of a causal relationship between women entrepreneurs and social responsibility practices in the Latin American context. This research captures the multidimensional nature of social responsibility by using a comprehensive and vast metric of CSR obtained from the data of the B Impact Assessment tool. This study illustrates how machine learning methods can be used to address the lack of structure of the Latin American B Impact Assessment data.

El propósito de esta investigación es identificar el efecto causal (no correlacional) del emprendimiento de mujeres en las prácticas de responsabilidad social empresarial (RSE) en América Latina.

Nos basamos en un conjunto de datos escasamente explorado hasta el momento sobre las Empresas B en América Latina para identificar la relación causal de interés, y utilizamos un método de Variables Instrumentales (VI) sintéticas.

Nuestros resultados verifican el efecto causal positivo del emprendimiento de las mujeres en la responsabilidad social. Descubrimos que un aumento del 1% en la proporción de mujeres emprendedoras conduce a un aumento de 0.5 en la puntuación de la Evaluación de Impacto B, nuestro indicador de RSE.

Contribuimos a la literatura proporcionando evidencia estadística sólida de una relación causal entre emprendedoras mujeres y prácticas de responsabilidad social en el contexto de América Latina. Esta investigación captura la naturaleza multidimensional de la responsabilidad social mediante el uso de una métrica amplia y vasta de RSE obtenida de los datos de la herramienta de Evaluación de Impacto B. Ilustramos cómo se pueden utilizar métodos de aprendizaje automático para abordar la falta de estructura de los datos de evaluación de impacto B en América Latina.

O propósito desta pesquisa é identificar o efeito causal (não correlacional) do empreendedorismo feminino nas práticas de responsabilidade social corporativa (RSC) na América Latina.

Baseamo-nos em um conjunto de dados escasso até então não explorado sobre as Empresas B na América Latina para identificar a relação causal de interesse, e utilizamos um método de Variáveis Instrumentais (VI) sintéticas.

Nossos resultados verificam o efeito causal positivo do empreendedorismo feminino na responsabilidade social. Descobrimos que um aumento de 1% na proporção de mulheres empreendedoras leva a um aumento de 0,5 no escore de Avaliação de Impacto B, nosso indicador de RSC.

Contribuímos para a literatura fornecendo evidências estatísticas robustas de uma relação causal entre empreendedoras mulheres e práticas de responsabilidade social na América Latina. Esta pesquisa captura a natureza multidimensional da responsabilidade social usando uma métrica abrangente e vasta de RSC obtida a partir dos dados da ferramenta de Avaliação de Impacto B. Ilustramos como métodos de aprendizado de máquina podem ser usados para lidar com a falta de estrutura dos dados de avaliação de impacto B na América Latina.