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This article aims to conduct a bibliometric study using structural and relational approaches amongst the extracted documents and investigates the connections between business incubation and entrepreneurship-related papers to describe the field comprehensively.

A total of 259 articles have been retrieved from Scopus database in order to conduct the bibliometric analysis. Performance analysis and science mapping techniques of bibliometrics have been used along with data visualisation software, i.e. VOSviewer and RStudio. The network collaboration and intellectual structures, i.e. bibliographic coupling, co-occurrence analysis, word cloud and trending topics, have been presented to identify the field’s latest trends, themes and development.

The findings highlight annual publication trends, including the most frequently cited articles, the most productive authors, countries and highly influential journals that contribute the most to the said field. The intellectual structures have been developed to identify research themes and trends by running co-occurrence analysis and bibliographic coupling. The findings of this study emphasize the value of technology transfer, mentorship programmes, entrepreneurship education and an emphasis on innovation and creativity through entrepreneurial universities and academia. These findings provide policymakers and administrative officials with crucial guidance for fortifying the pillars of entrepreneurship and education for the comprehensive development of the economy. Further, this article attempts to identify the most influential and relevant publications as well as the newest trends in the area of business incubation in combination with entrepreneurship.

The article contributes not only to broaden the scope of knowledge on the said research discipline but also to comprehend how the field has evolved over a period of time. This study also attracts the interest of scholars/academicians, leading to the significant production of scholarly documents in business incubation and entrepreneurship.

The field of entrepreneurship and business incubation is one of the important pillars for the growth and development of the economy. This piece contributes to this arena by focusing on the areas that must be taken care of by developing the entrepreneurial ecosystem and fostering the progress of startups. The fundamentals of this research highlight the importance of mentorship programs, entrepreneurship education, technology transfer and a focus on innovation and creativity through entrepreneurial education and efforts by universities/academia, giving an important direction to the policymakers and administration for strengthening the pillar of entrepreneurship and education for the holistic development of the economy.

Business incubation is an emerging field of academic research connected to startups, venture formation and entrepreneurship ecosystems, making it a potential scholarly discipline. This study presents a thorough bibliometric analysis over the last three decades, offering comprehensive details on the most significant developments in the field of business incubation. Moreover, the various analytical methods applied to this study make it more attractive.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

This paper aims to identify impediments, discuss impediments and make recommendations for the impediments during the execution of a search and seizure warrant for digital evidence in South African criminal cases.

The discussion of this article, the second article of two, focuses on a literature review of international and local impediments identified in case law and published research literature and how it is approached in various jurisdictions.

This study found that impediments identified and addressed internationally during the execution of a search and seizure warrant for digital evidence are relevant to South African criminal cases and still need to be addressed during the execution of a search and seizure warrant for digital evidence in South African criminal cases.

Although searches and seizures for digital evidence are relevant to civil, regulatory and criminal investigations, this study focuses on the search and seizure for digital evidence in criminal matters with an emphasis on the provisions of the Criminal Procedure Act 51 of 1977 and the Cybercrimes Act 19 of 2020.

The originality of this paper lies in the procedures followed during the physical search and seizure of digital information during the execution of search and seizure warrants for digital information in South Africa. If the South African Police Service follows the recommended procedures, it will contribute to the success of the South African Police Service, which would result in the improved quality of investigations and successful prosecution of crime in South Africa.

This article advocates that privacy literacy research and praxis mobilize people toward changing the technological and social conditions that discipline subjects toward advancing institutional, rather than community, goals.

This article analyzes theory and prior work on datafication, privacy, data literacy, privacy literacy and critical literacy to provide a vision for future privacy literacy research and praxis.

This article (1) explains why privacy is a valuable rallying point around which people can resist datafication, (2) locates privacy literacy within data literacy, (3) identifies three ways that current research and praxis have conceptualized privacy literacy (i.e. as knowledge, as a process of critical thinking and as a practice of enacting information flows) and offers a shared purpose to animate privacy literacy research and praxis toward social change and (4) explains how critical literacy can help privacy literacy scholars and practitioners orient their research and praxis toward changing the conditions that create privacy concerns.

This article uniquely synthesizes existing scholarship on data literacy, privacy literacy and critical literacy to provide a vision for how privacy literacy research and praxis can go beyond improving individual understanding and toward enacting social change.