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Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

With a view to ensuring the effective and efficient delivery of construction projects, this study evaluates the critical success factors (CSFs) for the adoption of gamification principles by construction professionals in developing countries, with an emphasis on Nigeria.

This study adopted a post-positivism philosophical approach, using a questionnaire survey to obtain quantitative data from 126 construction professionals in Lagos State. The data obtained were analyzed using frequencies, percentages, mean item scores (MIS), Kruskal–Wallis H-test and principal component analysis (PCA) as part of the exploratory factor analysis (EFA).

The findings indicated that the most significant factors for the adoption of gamification principles in the construction industry were “clear game mechanics and rules,” “incentives and rewards for users,” “secure and reliable technology infrastructure,” “real-time progress tracking and feedback” and “clear and measurable objectives.” Employing factor analysis, these CSFs were subsequently grouped into three primary clusters, namely “relevance and user experience,” “technology and support” and “integration and process.”

These findings not only enrich the existing theoretical framework but also provide a solid foundation upon which researchers can build for further theoretical development. This study also offers valuable insights that can inform and improve practical applications of gamification within the construction industry.

While prior research has explored gamification in various contexts, the unique contribution of this study is the thorough investigation of CSFs for gamification adoption specifically within the construction industry. In essence, this study fills a critical gap in the literature by offering fresh perspectives and tailored solutions for the construction industry's specific gamification needs.

The purpose of this study was to perform sentiment analysis and analyze the growth and popularity of Drupal, Joomla and WordPress on YouTube over a four-year period. This included identifying the most liked and commented videos for each content management system (CMS), ranking the CMSs based on the number of positive comments they received, and using natural language processing techniques to identify the top ten most frequently appearing words in videos about the CMSs.

The data for assessing the features of the videos of Drupal, WordPress and Joomla was extracted using Webometric Analyst version 4.4. with the help of the YouTube application programming interface key for videos on the selected CMSs uploaded from 2019 to 2022. The extraction of comments and sentiment analysis for the relevant videos was done using Mozdeh.

This study scrutinized 371, 234 and 313 videos of WordPress, Joomla and Drupal on YouTube. The findings reveal that there is a chronological growth of videos of the three CMSs in four years and till the present time, WordPress has the highest number of videos followed by Drupal and then Joomla. Regarding the ranking of highly liked videos, WordPress again wins the list with the highest number of likes in its videos followed by Drupal and then Joomla. For analyzing sentiments of the total comments extracted 123,409 for WordPress, 1,790 for Joomla and 1,783 for Drupal, respectively, WordPress receives the highest average positive comments followed by Drupal then Joomla. In top word frequency, the word “thank” highly occurs and viewers are asking for more tutorial videos.

To the best of the authors’ knowledge, this study is the first attempt for analyzing the sentiments of WordPress, Drupal and Joomla using Mozdeh software within the concerning period.

Despite to useful relevance to better understand how group-level identity develops, few studies have explored the identity theory in the esports field and, in particular, considering the impact of a fan's role identity. The current study aims to explore esports fan role-identity vis-à-vis the relationship with the sponsor and the sponsee so as to understand the effects on their behavioral intentions.

Using a sample of 356 esports fans who attended the 2021 FPF eFootball Open Challenge, a Confirmatory Factor Analysis (CFA) analyzed the psychometric properties of the constructs and a subsequent Structural Equation Modeling (SEM) examined the effects of fan identity on two types of behavioral intentions and sponsor–sponsee relationship.

Results indicate that fans who highly identify with esports have the highest attachment to the event and tend toward having a positive word-of-mouth intention. Esports fans who have a higher brand identification reported a positive attitude toward the event's sponsor brand and tend to purchase its products. Moreover, the study findings also provide evidence of the bidirectional interaction between the way in which fans attach with the esports event and its sponsor brand, leading to greater reciprocity in their identity formation.

This study helps to understand how the fan identity process can enhance its fate and develop mutually, building role overlapping identity in the esports sponsor–sponsee relationship. Complementarily, it supports of how the marketeers and managers must analyze the importance of being a fan to the individual in order to understand how its self-identity can shape the future behavior.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

Traceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models.

This research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production.

Together with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability.

The existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products.

The paper covers the topic of power strategies between actors and the interplay between the service ecosystem and the actor(s), and vice versa. The paper addresses the lack of conceptual development concerning power considerations beyond dyadic, rigid and role-based models found in general marketing literature. Further, the paper opens the area of power relationships, using the service ecosystem as conceptual framework.

The paper has a systemic and sociological view on service-ecosystems using mainly Giddens' structuration theory. Service-dominant logic literature from 2004 to 2021 is systematically reviewed for power issues and qualitatively analyzed. Mayring's step model of, firstly, inductive and, secondly, deductive category development is applied. Subcategories were identified, subsumed and finally grouped into five categories to increase the level of abstraction.

The article investigates power considerations and enables marketers to create power through (1) imbalance, to find strategies and counterstrategies for (2) actor's behavior, to understand the (3) actor's embeddedness within a service ecosystem and its dynamic nature, to learn about (4) institutions and actor's institutional work. A set of seven propositions is presented for the conceptualization of power strategies in a service ecosystem.

The consideration of power on different levels supports both the zooming-in and zooming-out to observe and understand the power phenomena in a service ecosystem. Seven propositions about episodic as well as systemic power relations are presented. Power is conceptualized in service ecosystem as transformative capability of an actor to intervene on institutions and in some way alter them, recognizing that power relations are co-created, dynamic and context-dependent.

The article recognizes different levels (micro-meso-macro) of power considerations and helps practitioners and marketers to create power through (1) imbalance, find strategies and counterstrategies for (2) actor's behavior, understand the (3) actor's embeddedness within a service ecosystem and its dynamic nature, learn about (4) institutions and actor's institutional work. This enables managers to find an appropriate choice of action in their specific context to transform the service ecosystem(s) they are embedded in.

As all social systems are power systems, a service ecosystem can only be fully understood by integrating the elementary concept of power. As such, power considerations within actor strategies and the service ecosystem are relevant to improve the understanding of transformation of the service ecosystem. Power, in the sense of the transformative capability of actors, changes the social and material world.

Power issues are important to understand the “hows” of resource integration in service ecosystems and its transformation or stability.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

The aim of this study is to explore how women in Sri Lanka cultivate entrepreneurial personae to navigate the various gendered roles they situationally enact, as they attempt to secure legitimacy and acceptance and overcome their otherness. Drawing on Goffman's theorisation of symbolic interaction, this study investigates how gender informs the performance of entrepreneurship in Sri Lanka. In this way, the study engages with the challenges women in the Global South navigate while undertaking entrepreneurship, and it contributes to the critical entrepreneurship literature on the intertwined nature of gender and entrepreneurship.

Following feminist standpoint epistemology (FSE), this qualitative study focuses on women entrepreneurs in Sri Lanka by examining the performance of entrepreneurship through 44 life history interviews (LHIs) and 40 Field Observations conducted over a seven-month period.

The findings reveal that women carefully cultivate entrepreneurial personae by striking a balance between entrepreneurial ideals and patriarchal social expectations around womanhood. The findings of the study present how the entrepreneurial personae are constructed by way of appearance, mannerism and setting, which presents opportunities for future research to explore the dramaturgical aspect of gender and entrepreneurship.

This study contributes to the growing body of feminist research surrounding women entrepreneurs, by drawing on insights from the lived experiences of women entrepreneurs in the Global South. This study also expands Goffman's theorisation of audience segregation and shows that a subject's understanding of the audience shapes their personae. A further contribution of this research is how space becomes an extension of the personae at play.