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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.

Signals, e.g. information released by firms about new products attract the attention and scrutiny of customers, competitors and other stakeholders. In product management, an important area of research focuses on the economic value of such signals. However, extant studies consider valuation effects of product signals independently, and largely ignore how the value of a product signal at launch depends upon prior preannouncements. This study aims to investigate how the dependence of new product development (NPD) signals on past preannouncements affects firms’ security prices.

The study develops a conceptual model that draws upon information asymmetry theories, i.e. signaling and agency theory to hypothesize the effect of firms’ product introduction announcements on security prices given two antecedent preannouncement types (costless and costly signals). Hypotheses are tested by conducting an event study analysis on a sample of 149 matched observations (product introduction announcement preceded by a certain type of preannouncement).

Empirical results confirm the hypothesis that positive valuation effects are observed during product launch that is preceded by initial costless product signaling. In contrast, for ex ante costly product signaling, launch events are not diagnostic enough to affect value. Since organizations’ NPD communications can revise investors’ prior beliefs, they need to be understood in more detail and managed strategically.

Valuation metrics can be noisy with a potential to influence information events. In addition, product introduction signals may be deployed more frequently in certain fast-paced industries, e.g. hi-tech.

Managers can incorporate signal dependence in product communications. For example, in costless ex ante product signaling situations, initial economic loss may be recovered through launch announcements. Furthermore, when costly signals have been used earlier, firms may economize on promotion costs during launch.

Past research has focused on assessing the economic value of new product signals independently, i.e. as discrete events. Absent is an examination of valuation effects due to the dependence of launch signals on prior preannouncements. This paper addresses the dependence gap, and empirical results show that even if firms do not deploy product signals ex ante, value can be created through ex post launch announcements.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

This study aims to analyze the platforming scenario at a Brazilian university as well as the data security process for students and professors.

This research brings an analysis through a qualitative approach of the platformization process in a Brazilian teaching institution.

The results point to a lack of knowledge on the part of teachers regarding data security in the platforming scenario, as well as the lack of effectiveness of institutions in protecting student data.

Within the Brazilian scenario, this research seeks to contribute to the discussion on platformization in view of the gaps and existing demands on this process in the country.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

This study aims to look at challenges that hinder small- and medium-sized enterprises (SMEs) from using open data (OD). The research gaps identified are then used to propose a next generation of OD platform (ODP+).

This study proposes a more effective platform for SMEs called ODP+. A proof of concept was implemented by using modern techniques and technologies, with a pilot conducted among selected SMEs and government employees to test the approach’s viability.

The findings identify current OD platforms generally, and in Gulf Cooperation Council (GCC) countries, they encounter several difficulties, including that the data sets are complex to understand and determine their potential for reuse. The application of big data analytics in mitigating the identified challenges is demonstrated through the artefacts that have been developed.

This paper discusses several challenges that must be addressed to ensure that OD is accessible, helpful and of high quality in the future when planning and implementing OD initiatives.

The proposed ODP+ integrates social network data, SME data sets and government databases. It will give SMEs a platform for combining data from government agencies, third parties and social networks to carry out complex analytical scenarios or build the needed application using artificial intelligence.

The findings promote the potential future utilisation of OD and suggest ways to give users access to knowledge and features.

To the best of the authors’ knowledge, no study provides extensive research about OD in Qatar or GCC. Further, the proposed ODP+ is a new platform that allows SMEs to run natural language data analytics queries.

Data from HCL company and their corporate entrepreneurship activities were sourced using interviews and discussions. Secondary data was collected from the company website and other information available in the public domain.

This case narrates activities undertaken by the Enterprise Technology Office (ETO) of HCL Technologies Infrastructure Service Division to build the Corporate Entrepreneurship function. Around 2015–2016, the ETO started engaging with multiple people and organizations associated with the technology ecosystem outside the firm boundary for novel technologies and solutions. These entities included venture capitalists and start-ups. The ETO also began engaging with internal teams and existing customers to promote the identified novel technologies and innovative solutions. The ETO function grew organically by hiring resources from internal and external pools. ETO also undertook specific programs to increase the involvement of internal teams in their initiatives, like the Joint Exploration Program. ETO explored several options to further the ecosystem innovation strategy and institutionalize corporate entrepreneurship activities. The ETO team deliberated on ways to align the stakeholder goals and evaluate if technology could play a role.

This case can be used in any course on “Managing Corporate Entrepreneurship and Innovation,” “Strategic Management of Technology Enterprises” or any course focused on managing technology and innovation for graduate (MBA) or executive participants.