Multiple Correspondence Analysis Research Articles

Using Latent Semantic Analysis to Investigate Wine Sensory Profiles—Application in Swedish Solaris Wines

Online text is a source of data in many fields, but it is yet to be explored by sensory scientists. The present work aimed to explore the suitability of using a bibliometric methodology such as Latent Semantic Analysis (LSA) to understand and define wine sensory spaces. Data were also explored by the more conventional Multiple Correspondence Analysis (MCA). The present work shows the potential use of LSA in sensory science; the first part of the study investigates the sensory profile of Swedish Solaris wines, while the second part focuses on understanding their fit with two international monovarietal white wines (Albariño and Chenin Blanc). The results show that the majority of Swedish Solaris wines could be associated with two different styles (LSA topics). However, there is no evidence of a cultivar typicality, as when comparing the Solaris wines with Albariño and Chenin Blanc, they shared features with both cultivars. Chenin Blanc was also found to be associated with different styles. In contrast, Albariño wines showed to have more unique features as the majority were associated with a single LSA topic.

ESCAPE study: Cannabidiol use in patients treated for substance use disorders, prevalence of use and characteristics of users.

Cannibidiol (CBD) is one of the two primary phytocannabinoids found in cannabis. Its diverse pharmacological properties suggest potential benefits for a wide range of medical conditions. The primary objective of this study was to estimate the prevalence of CBD use among patients being treated for substance use disorder. Additionally, we aimed to characterize the pattern of CBD use and describe the profiles of users. Patients admitted to two substance use disorder departments between May 6, 2020, and July 23, 2021were included in the study. Sociodemographic, medical and substance use data, including CBD use, were collected. A multiple correspondence analysis (MCA) was performed to identify substance uses most frequently correlated with CBD use. Multivariate analyses were also conducted to identify factors associated with CBD use. The prevalence of CBD use in the past 12 months among the 183 patients was 38.2%. However, at the time of the study, 63% had stopped using CBD, primarily due to its perceived ineffectiveness and cost. CBD was commonly consumed via inhalation, either by smoking the plant or using e-cigarette liquids. The MCA revealed that while there is no typical profile of CBD users, they were more likely to use sedative substances rather than stimulants. CBD users were younger and more likely to have psychiatric comorbidities. The prevalence of CBD use was notably high. Given the significant risk of drug interactions with CBD and its wide variability in composition, it is crucial for clinicians to routinely inquire about CBD use. The potential for self-therapeutic or recreational use in these patients appears to be underestimated.

Aging in Place in a Depopulated, Mountainous Area: The Role of Hometown-Visiting Family Members in Shimogo, Japan

In this study, we examined the factors that contribute to successful aging in place in the town of Shimogo, a depopulated mountainous area in Japan, focusing on the interplay between household support types, the performance of daily activities, and the involvement of geographically dispersed family members. A mixed-methods approach was used, combining survey data with multiple correspondence analysis and cluster analysis. The resident household findings reveal significant differences in community engagement and reliance on community support across three household support types: self-help, mutual aid, and combined support. The self-help households demonstrated high self-sufficiency and community participation, while the mutual-aid and combined-support households exhibited greater reliance on family and community resources. An analysis of the activities of out-migrated relatives uncovered diverse engagement profiles, motivated by both providing practical support and strengthening family bonds. The visit frequencies and activity types of the relatives were significantly influenced by both their own characteristics and the levels of self-reliance and community support needs of the resident households. Four distinct relative engagement clusters emerged, reflecting varying levels of support, community involvement, and visit frequency. Our findings underscore the crucial role of both informal (family) and formal (community) support networks in facilitating successful aging in place in rural depopulated areas. Policy implications include strategies supporting both self-reliance and the diverse mechanisms of family- and community-based support to ensure the sustainability of life in these challenging environments.

Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

Divergences and convergences across European musical preferences: How taste varies within and between countries

When investigating relational structures in culture, research in Europe has often either mapped the relationship between cultural tastes in a particular context, or mapped differences in cultural tastes (measured consistently) in different countries, without assessing how these differences can vary across them. Indeed, the idea of national homology (namely that the structures of cultural capital would be fairly similar in nations across Europe) has never been really tested, probably due to a lack of cross-national research on cultural preferences. Using data from the EUCROSS survey that took place in Denmark, Germany, Italy, Romania, Spain and the UK (2012–2013, n = 6016), we first use multiple correspondence analysis to estimate the relationships between a set of items on musical tastes. We then extend this through the use of class-specific analysis, to investigate how these relationships vary in each of the six countries. Finally, we analyse the relationships between the underlying dimensions of music tastes and different components of cosmopolitanism, compared with key demographic variables. We show that the musical field significantly varies across the nations represented in the survey, demonstrating that musical preferences remain largely anchored in national contexts. Cultural preferences are shaped by historical and social dynamics specific to each country, with significant variations in the symbolic value and demographic associations of music genres.

Psychosocial risks faced by olive workers in Spain: evaluated using the ‘Mini Psychosocial Factors’ questionnaire

BackgroundSpain has the largest olive-growing area in Europe. The goal of the present research is to evaluate the psychosocial risks faced by workers in the olive groves of Jaén (the...

Joint Correspondence Analysis: A Case Study on Life Satisfaction in Türkiye

Correspondence analysis is used both as a numerical and mostly as a graphical analysis when the data set is expressed as tables that contain frequencies or counts. It enables researchers to comprehend and interpret the relationship between the variables included in a contingency table. Simple correspondence analysis explores the relationship between two variables in a contingency table. However, most practical situations involve more than two variables, for which multiple correspondence analysis is employed to investigate the relationships among them. Joint correspondence analysis is proposed as an improvement of the multiple correspondence analysis. The aim of this study is to analyze and interpret the data from the survey of life satisfaction in Türkiye through joint correspondence analysis and alternative multiple correspondence analysis.

Exploring the impact of design elements on wrong-way driving incidents at partial cloverleaf interchange terminals

The complex geometric layout of partial cloverleaf (parclo) interchanges elevates the chances of drivers inadvertently choosing the incorrect ramp or direction. Previous research on wrong-way driving (WWD) has emphasized the frequent incidents at the off-ramp terminals of parclo interchanges. Many WWD incidents that did not result in a collision haven't received enough attention from researchers. Geometric features such as ramp design, signage placement, and interchange layout significantly influence driver behavior and decision-making. In this study, a comparative analysis was conducted to explore the correlation between geometric design elements and their impact on WWD incidents at parclo interchange terminals. Over 5000 h of video footage were meticulously reviewed to document instances of WWD at 75 parclo interchange terminals across 13 states. Multiple correspondence analysis was applied to explore the characteristics of the locations with recurring WWD incidents. The results revealed numerous associations among design elements that contributed to the increased risk of WWD incidents, including uncontrolled ramp terminal intersections without street lighting on a two-lane crossroad, poor pavement marking on a wide median between the on- and off-ramps, and so on. The outcomes of this study can be helpful in identifying and improving the critical geometric design criteria in parclo interchanges to minimize WWD incidents.

Cost Analysis of Penitentiary Systems and Comparison Between the Countries of the Council of Europe

Background: The objective was to analyze the budgets invested in prisons by the member states of the Council of Europe (CoE) and the relationships between the global cost, the cost incurred per single inmate, the number of inmates per 100,000 inhabitants (PPR), the gross domestic product (GDP) and per capita GDP. Methods: The data relating to the variables considered for the year 2020 were obtained from the SPACE-I 2021 of the CoE, the World Bank/OECD, and Eurostat. Regression models were used to evaluate the relationships between the PPR and the GDP, the daily cost per prisoner and per capita GDP, and between the PPR and the per capita GDP. A multiple correspondence analysis was performed to evaluate associations between the PPR, EU membership, cost per day, cost rate, geographical area, and inmate gender. Results: The daily expenditure per inmate in northern European countries reaches very high values, respectively: EUR 330.6 (Norway) and EUR 303 (Sweden), while, in the eastern countries, the values drop sharply (EUR 6.50 in Bulgaria and EUR 8.08 in Azerbaijani). The lowest PPR values are found in northern European countries, and the highest in the following countries: Russia, Turkey, Georgia, and Azerbaijan. Conclusions: Countries with a higher GDP per capita tend to have lower prison population rates and to invest larger amounts of funds for prison systems.

The space of immigrant discourse in the European press

In this article we offer a new analysis of two never previously combined datasets on press coverage of the “refugee crisis” in Europe in 2015. Consisting of twenty-nine newspapers from eleven countries, the data provides an unprecedented sample of the European press in the time of crisis. Using forty-six characteristics of frames, agents, aids and protective measures mentioned in the articles, we demonstrate an innovative analytic approach, where multiple correspondence analysis is used to construct and explore main differences in an European statistical space of articles (N=1674) and a sevenfold statistical typology of stories concerning the crisis. The findings indicate that while national and regional differences in the coverage are salient in explaining the balance of humanitarian or securitisation attitudes, it is the intra-national differences that emerge as particularly significant, revealing rich complexity of texts and contexts. Analysing the structuration of stories as space of press coverage allows us to move beyond isolated variables and look at how individual articles are more divided in their fundamental narratives.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Knowledge, attitudes, and practices towards rabies: A preliminary cross-sectional appraisal in Colombia.

Rabies virus infection can cause fatal brain disease in mammals. Any species is susceptible to infection. Any effort aimed at recognizing infected animals and performing first actions in the event of transmission is mandatory. To determine knowledge, attitudes, and practice profiles regarding rabies, or hydrophobia, in a municipality of Colombia, using a multiple correspondence analysis. A descriptive observational study involving 71 pet owners was carried out in the municipality of Ibagué (Tolima). A questionnaire-based survey collected data on rabies knowledge, attitudes, practices, and demographic information. The survey was conducted between October and November 2021, and the data analysis involved descriptive statistics and multiple correspondence analysis. The study revealed a commendable level of rabies awareness among the urban residents in the study municipality. However, there are causes of concern as they allow stray animals to enter their homes and are not aware of the importance of notifying dead animals. The respondents demonstrated a humane approach to bite management and emphasized the importance of wound cleaning. Additionally, they expressed a strong desire for more information to enhance their knowledge and awareness of the disease. The findings of this study provide valuable insights for improving rabies prevention efforts and promoting public health. Health education, evidence-based strategies, and community participation are essential for successful disease control and educational gaps addressing related to sociocultural factors.

Swedish Erasmus students' valuations of study destinations: how countries, cities, and institutions relate to choices and experiences

ABSTRACT Based on a survey of Swedish participants in the Erasmus+ exchange programme from the 2014 and 2015 application years, our study uses specific Multiple Correspondence Analysis to explore students’ valuations of their study destinations across different scales – country, city, and institution. Our findings indicate that places were valued based on three types of logic – academic, cultural, and labour market – where each type of logic emphasised each scale differently. First, the choice of the receiving institution relates to the country of destination. Students who primarily valued culture, language, and geographical location were overrepresented in Mediterranean countries, while students who emphasised the quality, reputation, and learning offer of the receiving institution were overrepresented in the Nordic countries. Second, the city matters in that students clearly preferred institutions in lively metropolises, which had a clear advantage over other mid-sized or smaller cities. Thirdly, the higher education institutions themselves play a role in study abroad choices. If the institutions are prestigious and high-ranked, this can outweigh a disadvantageous location. These types of logic tend to overlap so that the most dominant and sought-after countries are more likely to have highly attractive cities and metropolitan regions where highly ranked higher education institutions are often also found.

To develop a classification system which helps differentiate cystic intraductal papillary neoplasm of the bile duct from mucinous cystic neoplasm of the liver

ObjectiveTo establish a classification system which differentiates cystic intraductal papillary neoplasm of the bile duct (cystic IPNB) from hepatic mucinous cystic tumors (MCN) based on their radiological difference. MethodsA total of 75 patients pathologically diagnosed as MCN and IPNB in two major hospitals from 2015 to 2024 were enrolled. Radiological features were recorded and compared between these two tumors. Variables with significant differences were included in multivariate logistic regression (LR) analysis. A decision model was built and simplified based on importance ranking of variables. K-nearest-neighbor (KNN) model was introduced to learn distribution of individuals in main dimensions based on multiple correspondence analysis (MCA) and predicted diagnosis. The diagnostic efficacy of the classification system and the KNN model was compared. ResultsSignificant differences existed in Dmax-IVC angle, septation, mural nodule, upstream and downstream biliary dilatation, communication with bile duct between MCN and cystic IPNB. Downstream biliary dilatation and communication with bile duct were highly specific for IPNB (specificity, 97.9 % and 100 %, respectively), which could independently diagnose IPNB. Among four significant indicators in LR analysis, upstream biliary dilatation and Dmax-IVC angle were used for a simplified decision model to attain good applicability. The KNN model based on MCA data achieved highest accuracy (0.910) when K = 11. Overall, the classification system achieved an AUC of 0.882 (0.95CI: 0.797–0.966), compared with 0.911 (0.95CI: 0.818–1.000) in the KNN model, which demonstrated no significant difference (p = 0.655) in differential performance. ConclusionThe classification system combining four important indicators had equivalent performance to KNN model in discrimination, which was simple and applicable for clinical practice, and also accessible on unenhanced examinations.

Identification and Validation of an Instrument to Measure Physicians‘and Nurses' Knowledge, Attitudes, and Practices Regarding Antibiotic Resistance and Antimicrobial Stewardship

Background: In Italy, the presence of a suitable instrument to measure the knowledge, attitudes, and practices of intensive care physicians and nurses with respect to antibiotic resistance and antimicrobial stewardship could not be identified Objectives: To validate the Italian version of the Ashiru-Oredope tool used by the European Centre for Disease Prevention and Control (ECDC) in 2019 to investigate intensive care nurses' and physicians' knowledge, attitudes, and practices (KAP) regarding antibiotic resistance. Methods: Content validity was assessed using the Item-Content Validity Index (I-CVI), Scale Level-Content Validity Index/Universal Agreement (S-CVI/UA) score, Scale Level-Content Validity Index/Average (S-CVI/Ave) score, and Average Content Validity Ratio (CVR). Multiple correspondence analysis (MCA) was then carried out together with cluster analysis (CA) to check the correspondence of the items with the eight subdivision domains of the scale. Results: The final version consisted of 55/60 deemed relevant items, with Item-Content Validity Index (I-CVI) scores ranging from 0,625 to 1. The Scale Level-Content Validity Index/Universal Agreement (S-CVI/UA) score was 0.43; the Scale Level-Content Validity Index/Average (S-CVI/Ave) 0.90. The mean CVR of the scale was 0.43. The inter-rater reliability (Cohen's K) was between good and excellent. The combination of MCA and CA confirmed the correspondence of the items with the domains. Conclusion: The Italian version is a reliable and valid instrument for investigating antibiotic resistance and antimicrobial stewardship among Italian intensive care nurses and physicians.

INTERNAL DYNAMICS AND INNOVATION: A CROSS-DISCIPLINARY REVIEW AND FUTURE RESEARCH AGENDA

Focusing on the internal dynamics of firms, this paper examines the interplay between human resource practices, absorptive capacity, and innovation. By systematically analyzing and synthesizing 100 papers from Elsevier's Scopus and Web of Science databases, this review uncovers and interprets their interconnections. We employed Multiple Correspondence Analysis to highlight the theoretical foundations and main research themes. This methodological approach facilitates the visualization of the foundational intellectual structure and the identification of new research opportunities, thereby fostering further synthesis and advancement. Following the analysis of the selected papers and in response to the need for a framework focusing exclusively on empirical studies, we developed an Antecedents-Drivers-Outcomes (ADO) framework based on empirical evidence from a sub-sample of 88 manuscripts. Based on the results of the multiple correspondence analysis and evaluations of empirical papers, this review outlines main theoretical foundations related to resource-based views, network and learning perspectives, and dynamic capabilities. It also identifies major research themes, including organizational culture, human capital empowerment, and development processes. Furthermore, the article highlights gaps and future research opportunities within these intersecting domains, acknowledges advancements in radical and process technological innovation, such as artificial intelligence and machine learning, and provides an overview of the latest developments.

COVID and consumers’ food strategies in Madrid, Spain: Towards a resilient city-region food system approach

Lockdowns, restrictions on people's movements and gatherings, and other COVID-19 containment measures have had economic, social, and environmental effects. The pandemic resulted in major disruption to food systems, highlighting serious weaknesses in the way such systems are currently configured. However, the pandemic also created important opportunities to develop transformative actions enabling the creation of more resilient and sustainable food systems. In this paper, we delve more deeply into the strategies developed by consumers, as key stakeholders in the food system, through an in-depth survey and statistical analysis of changes in consumers' habits during the pandemic lockdowns. We adopt a city-region food system (CRFS) approach to explore ways to enhance food system resilience to future crises.Data were collected through a telephone survey of a representative sample (600 consumers), conducted in the region of Madrid in May 2021. First, descriptive analyses allowed different consumer strategies to be identified. Differences in food preferences and behaviour changes between individuals in the sample were analysed by age, gender, income, and level of education using exponential family Principal Component Analysis (EPCA), logistic regression, and Multiple Correspondence Analysis (MCA). Lastly, Principal Component Analysis (PCA) was applied to classify the consumer strategies into four discrete profiles.The findings show significant differences in consumers' responses according to age, income, and level of education. Additionally, the results highlight increased consumption of local and organic products, and online shopping, as typical strategies employed by consumers to respond to the COVID-19 pandemic. In this context, alternative food networks were shown to play a key role in enhancing food security. Interestingly, no significant differences were found between urban and rural areas. Our discussion addresses this apparent homogeneity in consumers’ habits from a resilient CRFS approach.

Pharmaceutical Company's Choices of Indication for the First Clinical Projects in Oncological Drug Development in the United States.

We analyzed factors shaping the choice of the lead indication (i.e., cancer type) in the first clinical development projects of new oncological drugs in the United States (US), and how the type of pharmaceutical company is related to this choice. We selected 576 new clinical development projects in the US since 2000 for analysis. These projects were characterized according to three potential perspectives detected by multiple correspondence analysis: the morbidity of the cancer type which corresponds to market size of each cancer type, the company's previous experience with the cancer type, and the company's attitude to development risks. Mega firms tend to choose cancer types with higher morbidity (and large-market), previously experienced cancer types, while diverse small firms choose both major and rare cancers and both high- and low-risk projects, indicating that different sizes of firms utilize different development entry patterns. Common tendencies concerning the choice of lead indication were found across all companies. Cancer types the company had developed and launched in the past were more likely to be chosen; cancer types with high five-year survival rates and those with high competition were less likely to be chosen. The study showed that pharmaceutical companies seem to enter clinical development from cancer types where they can demonstrate their strengths and advantages through experience, depending on each cancer type's different market sizes and development difficulties. The results could provide clues for considering what support measures and incentives are appropriate to balance the efficiency of industrial development and the fulfillment of society's unmet medical needs.

Strategies at the platform-train interface: in the pursuit of psychological comfort?

ABSTRACT Platform-train interface (PTI) is a shared space with several interactions between travellers. These interactions lead to congestion, delays, safety concerns at PTI and resulting in a negative experience for users. Understanding individual behaviour is crucial to improving pedestrian flow at stations and users’ experience, to fine supporting public transport use. This study aims to understand how individuals experience and behaviour at PTI. Specifically, we investigate psychological comfort and its link with approach/avoidance strategies (AAS) used by travellers at PTI. Based on the critical incidents technique, 22 interviews were conducted. Participants were asked to describe multiple trips according to their valence (positive v. negative). Seventy-seven critical incidents representing 125 route sections involving a station were collected, coded and analysed through multiple correspondence analysis (MCA), to determine reasons of incidents’ valence. The descriptive analysis of the 125 sections aims at identifying AAS used by individuals at PTI. Results suggest that reasons of the valence of the trip (e.g., security or density) could be related to the different psychological comfort’s dimensions. AAS reported seem to be aimed at increasing psychological comfort. Finally, we observed that while crowds usually link to avoidance strategies, in specific cases of uncertainty travellers also reported approaching a crowd.

Students' perceptions of skills needed for labor in digital economy

This study examines the skills required for individuals to thrive in the digital economy and emphasizes the significance of education and training in developing these skills. The study focuses on four key skills: information technology (IT) skills, creative thinking skills, teamwork skills, and problem-solving skills. This study explores the definition and impact of the digital economy on workers, economic development, job creation, and sustainability. The research employed multiple correspondence analysis and random forest regression to analyze the data and evaluate the research model. The results demonstrated the reliability and meaningfulness of the scales used in the study, with evaluation metrics indicating a high level of accuracy in the research model. The article concludes by emphasizing the importance of equipping individuals with the necessary skills to succeed in the digital economy, and suggests future research directions.