Mohamed A.K. Basuony, Ehab K.A. Mohamed, Ahmed Elragal and Khaled Hussainey
This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four…
Abstract
Purpose
This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.
Design/methodology/approach
A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.
Findings
The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.
Practical implications
This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.
Originality/value
To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.
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Marian H. Amin, Ehab K.A. Mohamed and Ahmed Elragal
The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the…
Abstract
Purpose
The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information.
Design/methodology/approach
This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation topic modeling to identify financial disclosure tweets. Panel, Logistic and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics.
Findings
Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity and board tenure.
Originality/value
The extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive vs non-executive directors relating to disclosure decisions.
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Aya Rizk, Anna Ståhlbröst and Ahmed Elragal
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope…
Abstract
Purpose
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.
Design/methodology/approach
A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.
Findings
Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.
Originality/value
The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.
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Ahmed Elragal and Nada El-Gendy
Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be…
Abstract
Purpose
Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions.
Design/methodology/approach
An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction.
Findings
The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point.
Research limitations/implications
The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper.
Practical implications
The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement.
Originality/value
The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach.
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Hani Al-Dmour, Nour Saad, Eatedal Basheer Amin, Rand Al-Dmour and Ahmed Al-Dmour
This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance.
Abstract
Purpose
This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance.
Design/methodology/approach
A conceptual framework was developed in this regard based on a comprehensive literature review and the Technology–Environment–Organization (TOE) model. A quantitative approach was used, and the data was collected from 235 commercial banks’ senior and middle managers (IT, financial and marketers) using both online and paper-based questionnaires.
Findings
The results showed that the extent of the practices of big data analytics applications by commercial banks operating in Jordan is considered to be moderate (i.e. 60%). The results indicated that 61% of the variation on the practices of big data analytics applications by commercial banks could be predicated by TOE model. The organizational factors were found the most important predictors. The results also provide empirical evidence that the extent of practices of big data analytics applications has a positive influence on the bank performance. In the final section, research implications and future directions are presented.
Originality/value
This paper contributes to theory by filling a gap in the literature regarding the extent of the practices of big data analytics applications by commercial banks operating in developing countries, such as Jordan. It empirically examines the impact of the practices of big data analytics applications on bank performance.
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Henrique Benedetto, Maurício Moreira e Silva Bernardes and Darli Vieira
The purpose of this paper is to propose a framework to assist in the estimation of effort for projects in the field of design. The study first seeks supporting material to outline…
Abstract
Purpose
The purpose of this paper is to propose a framework to assist in the estimation of effort for projects in the field of design. The study first seeks supporting material to outline an understanding of how design professionals have access to time estimation information for quoting their projects.
Design/methodology/approach
The work was based on in-depth interviews conducted with 13 professionals from various design sectors that focused on understanding important elements of the project quotation process. Content analysis was performed on the information provided, and four dimensions were identified. A framework that included these dimensions was designed and validated using a focus group composed of professionals involved in project quotation. The framework includes the generation of a project network structure; identifying tasks and their duration for each design activity; and the ways in which this information remains updated and evolves through the incorporation of dynamic systems concepts.
Findings
The results of this study will be the production of an external knowledge base that designers can use as a basis for performing their profession.
Originality/value
This study is relevant because there is no information source that addresses tasks and associated durations on which design professionals can rely for the development of quotations.
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Mahmoud El Samad, Sam El Nemar, Georgia Sakka and Hani El-Chaarani
The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of…
Abstract
Purpose
The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.
Design/methodology/approach
This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.
Findings
This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.
Practical implications
This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.
Originality/value
This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.