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The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

The current advancements in technologies and the internet industry provide users with many innovative digital devices for entertainment, communication and trade. However, simultaneous development and the rising sophistication of cybercrimes bring new challenges. Micro businesses use technology like how people use it at home, but face higher cyber risks during riskier transactions, with human error playing a significant role. Moreover, information security researchers have often studied individuals’ adherence to compliance behaviour in response to cyber threats. The study aims to examine the protection motivation theory (PMT)-based model to understand individuals’ tendency to adopt secure behaviours.

The study focuses on Australian micro businesses since they are more susceptible to cyberattacks due to the least security measures in place. Out of 877 questionnaires distributed online to Australian micro business owners through survey panel provider “Dynata,” 502 (N = 502) complete responses were included. Structural equational modelling was used to analyse the relationships among the variables.

The results indicate that all constructs of the protection motivation, except threat susceptibility, successfully predict the user protective behaviours. Also, increased cybersecurity costs negatively impact users’ safe cyber practices.

The study has critical implications for understanding micro business owners’ cyber security behaviours. The study contributes to the current knowledge of cyber security in micro businesses through the lens of PMT.

This study examines the effect of digitalization on poverty and income inequality in developing countries. The study answers the question of whether digitalization is a way for developing countries to get out of poverty and income inequality.

The study uses data from 17 developing countries with data from 2005 to 2021. The study employs fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS), with an augmented mean group (AMG) for robustness. Digitalization, as the variable of interest, is proxied by the digitalization index (DI), constructed using principal component analysis (PCA). The dependent variables are poverty and income inequality, which are used in different models.

The evidence indicates that digitalization decreases poverty and income inequality in developing countries. These findings are justified when we use the AMG estimator, but the strength of the coefficients and significance levels are higher in the FMOLS and DOLS estimators. The results of the control variables also show that human development (LHDI), CO2 emissions and foreign direct investment (FDI) have decreasing effects on poverty and income inequality. Thus, digitalization is a good option for developing countries to get out of poverty and income inequality to achieve sustainable development goals (1&10).

This study provides rigorous empirical evidence on the effect of digitalization on poverty and income inequality in developing countries. Unlike the previous studies on developing countries, this study used a DI to proxy digitalization. In addition, the authors use FMOLS and DOLS estimators, with an AMG estimator for robustness, to provide long-run coefficients.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0586

Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. However, there seems to be limited research on exploring the impact of specific privacy concerns on users’ intention to engage in various privacy protection behaviors. This study aims to examine the role of social privacy concerns, institutional privacy concerns and privacy self-efficacy as antecedents of privacy protection–related control activities intention among young adults active on SNS.

Data collected from 284 young adults active on SNS was analyzed through partial least squares structural equation modeling using Smart PLS.

The results indicate that institutional privacy concerns, social privacy concerns and privacy self-efficacy positively influence the control activities intention of SNS users. The extent of privacy self-efficacy and privacy protection-related control activities intention differs among users based on gender.

This study is limited to a population of young adults in the age group of 18–25 years.

The findings of this study form the basis for specific recommendations addressing the different types of privacy concerns experienced by social media users, promoting responsible privacy control behaviors on online platforms and discouraging the possible misuse of information by third parties.

This study validates a theoretical framework that can contribute to future investigations concerning the use of SNS. The study findings form the basis for a set of practical recommendations for policymakers, SNS platforms and users.

Sharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.

The study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.

The study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.

This study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Science and technology are transforming our world in ways that have not been seen in a long time, and we live in a rapidly changing world. Despite these changes, as citizens of today, we must not lose sight of the reality that these changes even cause crises that must be managed in order to place ourselves in a true working environment that allows us to survive as employees despite these changes. The main objective of this research is to find the relationship between interpersonal competences and teacher performance in a sustainable university. The methodology used was the quantitative, nonexperimental approach, as the variables will not be deliberately manipulated. In order to verify whether or not there was a relationship between these variables, 84 teachers from the Universidad Nacional Santiago Antunez de Mayolo were surveyed to evaluate the variables under study. The results obtained show a direct and significant relationship (Spearman's Rho = 0.731) between the two variables. Through this research, it was possible to determine that teachers who have developed interpersonal competences have a better performance, while in the relationships between interpersonal competences and the dimensions of teacher performance, a positive correlation was obtained.

The rapid expansion of internet usage and device connectivity has underscored the importance of understanding the public’s cyber behavior and knowledge. Despite this, there is little research that examines the public’s objective knowledge of secure information security practices. The purpose of this study is to examine how objective cyber awareness is distributed throughout society.

This study draws on a large national survey of adults to examine the relationship between individual factors – such as demographic attributes and socioeconomic resources – and information security awareness. The study estimates several statistical models using weighted logistic regression to model objective information security awareness.

The results indicate that socioeconomic resources such as income and education have a significant effect on individuals’ information security awareness with richer and more highly educated individuals exhibiting greater awareness of important security practices and tools. Additionally, age and gender represent consistent and clear informational gaps in society as older individuals and females are significantly less knowledgeable about an array of information security practices than younger individuals and males, respectively.

The findings have important implications for our understanding of information security behavior and user vulnerability in an increasingly digital and connected society. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks. While digital technology will continue to permeate many aspects of daily life – from financial transactions to health services to social interactions – the findings here indicate that some users may be far more exposed and vulnerable to attack than others.

This study contributes to our understanding of general user information security awareness using a large survey and statistical models to generalize about the public’s information security awareness across multiple domains and stimulates future research on public knowledge of information security. The findings indicate that some users may be far more exposed and vulnerable to attack than others. Despite the growing importance of cybersecurity for all individuals in nearly all domains of daily life, there is substantial inequality in awareness about secure cyber practices and the tools and techniques used to protect one’s self from attacks.

The rise of cryptocurrencies and other digital assets has triggered concerns about regulation and security. Governments and regulatory bodies are challenged to create frameworks that protect consumers, combat money laundering and address risks linked to digital assets. Conventional approaches to confiscation and anti-money laundering are deemed insufficient in this evolving landscape. The absence of a central authority and the use of encryption hinder the identification of asset owners and the tracking of illicit activities. Moreover, the international and cross-border nature of digital assets complicates matters, demanding global coordination. The purpose of this study is to highlight that the effective combat of money laundering, legislative action, innovative investigative techniques and public–private partnerships are crucial.

The focal point of this paper is Australia’s approach to law enforcement in the realm of digital assets. It underscores the pivotal role of robust confiscation mechanisms in disrupting criminal networks operating through digital means. The paper firmly asserts that staying ahead of the curve and maintaining an agile stance is paramount. Criminals are quick to embrace emerging technologies, necessitating proactive measures from policymakers and law enforcement agencies.

It is argued that an agile and comprehensive approach is vital in countering money laundering, as criminals adapt to new technologies. Policymakers and law enforcement agencies must remain proactively ahead of these developments to efficiently identify, trace and seize digital assets involved in illicit activities, thereby safeguarding the integrity of the global financial system.

This paper provides a distinctive perspective by examining Australia’s legal anti-money laundering and counterterrorism financing framework, along with its law enforcement strategies within the realm of the digital asset landscape. While there is a plethora of literature on both asset confiscation and digital assets, there is a noticeable absence of exploration into their interplay, especially within the Australian context.