Digital Transformation for a Sustainable Society in the 21st Century

Social commerce is emerging as an important platform in e-commerce. It brings people to the comfort zone to buying and selling product that they cannot reach physically. The purpose of this research is to review the empirical research on social commerce published between 2012 to 2019. The paper mainly reviews the theories and models used in this area and limitations acknowledged by studies in social commerce area. The findings indicated that TAM, social support theory and S-O-R model are some of the most frequently used models. Also, use of biased sample, limited factors and cross-sectional studies are some of the most common limitations used across majority of studies.

Companies and their brands initiated various Co-creation practices on social media. Co-creation improves value for both companies and their customers. This study explored the customer perspective of interest for participating in such Co-creation opportunities on Facebook. Drawing upon the Use and Gratification Theory we investigated the intention of customers to take part in Co-creation on Facebook. We related people’s Co-creation behaviour to the expectancy of satisfaction or reward for their actions. Customer Engagement is an additional concept that expresses the emotional attachment of customers to brands and companies. Underlying reasons were investigated why customers would consider taking part in Co-creation. The quantitative survey inquired customers about their expected Benefits, level of Customer Engagement and their intention for taking part in Co-creation with companies on Facebook. Our results showed that Customer Engagement can be considered as the most important predictor for the intention to Co-create instead of User Gratification. Hedonic Benefits are the most important drivers for User Gratification, playing a key role in the people’s intention for taking part in Co-creation practices. Overall, people’s intention for Co-creation on Facebook increases when they have a meaningful and pleasurable way of experiencing the companies’ products that concern them.

Mobile payment (m-payment) apps have been introduced as an innovative alternative payment method that extends in-store payment options available to consumers. Despite being marketed as convenient and secure, recent research reports that m-payment uptake has gone far below earlier forecasts. This is due to consumers perceiving little added value relative to existing payment options, such as contactless cards. Augmenting m-payment with value added services (VAS) has been suggested as a way to add value to m-payment and boost demand. However, empirical investigation about the role and effect of VAS on consumers’ perceptions of m-payment value remains scant. This study attempts to fill this gap by employing a deductive qualitative approach through the lens of perceived value theory, extended with perceived trust and risk. A total of 23 interviews were conducted with UK adopters and nonadopters of m-payment. The findings suggest that the perceived added value of the augmented m-payment service was mainly derived from utilitarian values associated with the additional functionalities offered by VAS. Additionally, the augmentation of m-payment has enhanced perceptions of trust in the service provider as a result of integrating additional features that tackle issues associated with the payment experience. This study advances knowledge of the concept of added value in the m-payment context and provides practical suggestions to m-payment providers for increasing the consumer perceived value.

In this paper, we identify two distinct behavioral strategies for dealing with problems encountered in the use of mobile apps - fight or flight. In the fight strategy, individuals do not give up using an app when faced with a problem; rather, they experiment with different ways to cope with that problem, whereas the flight strategy refers to the user’s decision to uninstall an app when they encounter a problem and/or their intention to use an alternative app. These strategies were identified from an analysis of documents, which forty-two users reported, and can be used to understand how users deal with encountered problems. The participants were asked to use a mobile app of their choice for one week and report the behavioral strategies they utilized to counter problems they experienced. According to the findings obtained from content analysis, the most reported complaints concerned the categories of interface design, functional error, feature request, and feature removal. The participants who complained about functional errors, frustrating features, and slow application speed stopped using the app (flight behavior) whereas those that were dissatisfied with the interface, a missing feature or the content of the app continued to use the app and tried to overcome the problems (fight behavior).

Despite the rapid growth in mobile stores (e.g., Apple Store, Google Play), scholarly research in this area is still in the early stages. In particular, there is a need for more empirical analysis of how the main features of these new systems shape the customer experience. This study aims to empirically identify and validate the key factors shaping users’ satisfaction toward mobile stores. The conceptual model was proposed based on a group of the main factors from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), mobile interactivity, and perceived security and privacy. The empirical analysis was conducted in Jordan by collecting data from a convenience sample of users of mobile stores. Structural equation modelling was applied to test the current study’s model. The results support the significant impact of performance expectancy, price value, hedonic motivation, personalization, responsiveness, and perceived security and privacy on user satisfaction. Discussion of the main limitations and future research directions are also provided.

Product reviews are a type of user-generated content that can be beneficial for both customers and product designers. Without quantifying the design knowledge present in product reviews, however, it is hard for the companies to integrate reviews into the design process. Several studies investigated review helpfulness in general, but few works explored the problem of review quality from the perspective of product designers. In this study, a theoretical model is presented and a system is proposed to assess the quality of online product reviews from the viewpoint of product designers. The system involves an original similarity-based metric to quantify the design information content of reviews on a continuous scale. Experiments are performed on a large number of digital camera reviews, with results indicating that the proposed system is capable of recognizing high-quality content, and would potentially assist companies in product improvement and innovation.

Most adults in industrialized countries now routinely check online reviews before selecting a product or service such as lodging. This reliance on online reviews can entice some hotel managers to pay for fraudulent reviews – either to boost their own property or to disparage their competitors. The detection of fraudulent reviews has been addressed by humans and by machine learning approaches yet remains a challenge. We conduct an empirical study in which we create fake reviews, merge them with verified reviews and then employ four methods (Naïve Bayes, SVMs, human computation and hybrid human-machine approaches) to discriminate the genuine reviews from the false ones. We find that overall a hybrid human-machine method works better than either human or machine-based methods for detecting fraud – provided the most salient features are chosen. Our process has implications for fraud detection across numerous domains, such as financial statements, insurance claims, and reporting clinical trials.

In light of the very high popularity of electronic word of mouth in the form of products and services reviews on the internet and its critical importance for businesses, the aim of this research is to investigate why customers who buy skincare products do not engage in eWOM by not leaving reviews about these products online. This research adopts a qualitative nature, using semi-structured in-depth interviews. Respondents’ group consists of the same amount of males and females who were asked questions regarding their electronic and traditional word of mouth experiences. Received responses demonstrate that laziness, lack of general interest in skin care and lack of feeling altruistic towards consumers online are the key factors preventing consumers from leaving reviews about skin care online.

Big data promises to drive economic growth and development, but if not applied across borders it can lead to a greater digital divide. There are, however, no indexes measuring big data readiness on country level. Most existing indexes cover topics such as digitalization and focus on developed economies with a clear underrepresentation of Africa. Thus, the underlying question to answer is: what are the required components for an index measuring big data readiness in Africa? The design science approach is used to design the Big Data Readiness Index (BDRI). The BDRI is developed in line with the academic definition of big data under the five V’s: volume, variety, velocity, veracity and value, and consists of 75 indicators, grouped into drivers that build up the five components. We apply this index to all African countries which yields, amongst others, country rankings that show relatively high BDRI levels for coastal countries such as South Africa, Kenya and Namibia as well as for islands such as Mauritius. Limitations and further development of the BDRI are discussed.

In an information society, information has become one of the most valuable asserts to an organisation. This is even more important in the mining industry in Africa where production lines are highly sensitive and decision makers are dependable on correct information to make decisions. One of the systems that can provide for the information needs of an organisation - Business Intelligence (BI) systems - unfortunately has a high failure rate. Some of the reasons can be attributed to technical issues (such as data structures, data warehouses), process issues (information retrieval processes and analysis), human issues (resistance to adoption) and the complex nature of BI.This qualitative study investigated the adoption of BI systems by end users by considering the work environment and user empowerment as suggested by Kim and Gupta [1]. Data was gathered using semi-structured interviews considering both aspects of the work environment and user empowerment.The findings of the study suggested that a strong bureaucratic culture and strict safety regulatory requirements inhibits job autonomy. Job autonomy in return has a negative impact on the willingness of end users to create their own BI reports. Poor management support and a lack of training in the utilisation of BI systems furthermore make it difficult for the ageing workforce to use all the advanced features of the BI systems and capabilities. Finally, end users felt a lack of empowerment to make business decisions and therefor lack motivation to use the system.

In this paper, we highlight how the value of data accumulates through the stages in a value chain. We introduce a Big data value chain where the value adding stages are decoupled from the technological requirements of data processing. We argue that through viewing the stages of value accumulation, it is possible to identify such challenges in dealing with Big Data that cannot be mitigated through technological developments. Our proposed Big Data value chain consists of eight stages that we subsequently cluster into three main phases, namely sourcing, warehousing and analyzing. In scrutinizing these three phases we suggest that the technologically immitigable challenges in sourcing relate to the veracity of data, and the challenges in warehousing concern ownership and power distribution. Finally, in the phase of analyzing the problems are manifold, including the black boxed nature of the algorithms, the problematics of standards of desirability, and the mandatory trade-offs. Our discursive article contributes to the literature discussing the value, utility and implications of Big Data.

Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

Big data has recently become the focus of academic and corporate investigation due to its high potential in generating business and social value. We have done a systematic mapping of the literature related to big data and its applications leading to social change through the lens of social innovation. The search strategy initially resulted in 593 papers, and after applying inclusion exclusion criteria a total of 156 papers were mapped; 59% of which were identified as empirical studies. This mapping investigated the publication frequency of the studies, research approach and contributions, research areas and article distribution per journal. We also address some challenges found from the mapping associated with the research topic. This mapping study will offer the basis for a reflection process among the researchers in this field and will allow us to develop a research agenda and roadmap of big data and its applications leading to social change.

The interest of adopting big data (BD) in organizations has emerged in recent years. Even though many organizations have attempted to adopt BD, the benefits gained by this investment has been limited, and organizations struggle to fully utilize the potential of BD. There has been done several studies on the different challenges and benefits that may occur during the process of adopting and using BD. One major factor that has been discussed in the literature to overcome such challenges has been organizational culture. This paper aims to provide a systematic literature review that reports the organizational culture’s impact on BD adoption through the lens of different cultural dimensions. This paper identifies the different dimensions of organizational culture and dives into the specific challenges and potential strategies/techniques to combat these challenges. By reviewing these papers, we ended up with identifying four dimensions of organizational culture in the context of BD. The result of this paper aims to help practitioners and researchers to understand and combat the specific challenges surrounding organizational culture when adopting BD.

To date, data science and analytics have received much attention from organizations seeking to explore how to use their massive volumes of data to create value and accelerate the adoption of Circular Economy (CE) concepts. The correct utilization of analytics with circular strategies may enable a step change that goes beyond incremental efficiency gains towards a more sustainable and circular economy. However, the adoption of such smart circular strategies by the industry is lagging, and few studies have detailed how to operationalize this potential at scale. Motivated by this, this study seeks to address how organizations can better structure their data understanding and preparation to align with overall business and CE goals. Therefore, based on the literature and a case study the relationship between data science and the CE is explored, and a generic process model is proposed. The proposed process model extends the Cross Industry Standard Process for Data Mining (CRISP-DM) with an additional phase of data validation and integrates the concept of analytic profiles. We demonstrate its application for the case study of a manufacturing company seeking to implement the smart circular strategy - predictive maintenance.

The world changes and new digital solutions arise in a fast pace, requiring organizations to adapt fast to these changes and innovations. Consequently, the required skills and knowledge of employees to effectively contribute to the core business of an organization, are also subject to changes. Currently, Human Resource (HR) professionals cannot acquire a sufficient insight into organizations’ human capital in order to align it with the current and future core processes of the organization. To acquire the necessary insights and knowledge, organizations see great potential in harnessing the wealth of data about their human capital and core processes. Hence, organizations have a great interest in HR Analytics, which is a rising field on the intersection of HR and big data. Despite the interest, there are only few efforts that focus on HR Analytics and its implementations. In this paper, we analyze the characteristics of the current HR function, present a framework that systematically exploits HR data in order to improve the HR function, and describe our implementation of parts of the presented framework. As such, our framework and its implementations can be regarded as a first step towards conducting HR Analytics in practice.

The public expects government institutions to open their data to enable society to reap the benefits of these data. However, governments are often reluctant to disclose their data due to possible disadvantages. These disadvantages, at the same time, can be circumstances by processing the data before disclosing. Investments are needed to be able to pre-process a dataset. Hence, a trade-off between the benefits and cost of opening data needs to be made. Decisions to disclose are often made based on binary options like “open” or “closed” the data, whereas also parts of a dataset can be opened or only pre-processed data. The objective of this study is to develop a decision tree analysis in open data (DTOD) to estimate the costs and benefits of disclosing data using a DTA approach. Experts’ judgment is used to quantify the pay-offs of possible consequences of the costs and benefits and to estimate the chance of occurrence. The result shows that for non-trivial decisions the DTOD helps, as it allows the creation of decision structures to show alternatives ways of opening data and the benefits and disadvantages of each alternative.

This paper focuses on demand forecasts for parking facilities. Our work utilizes open parking data for predictions. Several cities in Europe already publish this data continuously in the standardized DATEX II format. Traffic related information will become more ubiquitous in the future as all EU-member states must implement real-time traffic information services including parking status data since July 2017 implementing the EU directives 2010/40 and 2015/962. We demonstrate how to extract reliable and easily comprehensible forecast models for future-parking demand based on open data. These models find multiple use cases not only on a business planning level and for financial revenue forecasting but also to make traffic information systems more resilient to outages and to improve routing of drivers directing them to parking facilities with availability upon predicted arrival. Our approach takes into consideration that the data constitutes irregular time series and incorporates contextual information into the predictive models to obtain higher precision forecasts.

The release of a growing amount of open procurement data led to various initiatives for harmonising the data being provided. Among others, the Open Contracting Data Standard (OCDS) is highly relevant due to its high practical value and increasing traction. OCDS defines a common data model for publishing structured data throughout most of the stages of a contracting process. OCDS is document-oriented and focuses on packaging and delivering relevant data in an iterative and event-driven manner through a series of releases. Ontologies, beyond providing uniform access to heterogeneous procurement data, could enable integration with related data sets such as with supplier data for advanced analytics and insight extraction. Therefore, we developed an ontology, the “OCDS ontology”, by using OCDS’ main domain perspective and vocabulary, since it is an essential source of domain knowledge. In this paper, we provide an overview of the developed ontology.

Recently, the topic of research data management (RDM) has emerged at the forefront of Open Science. Funders and publishers posit new expectations on data management planning and transparent reporting of research. At the same time, laboratories rely upon undocumented files to record data, process results and submit manuscripts which hinders repeatable and replicable management of experimental resources. In this study, we design a forensic process to reconstruct and evaluate data management practices in scientific laboratories. The process we design is named Laboratory Forensics (LF) as it combines digital forensic techniques and the systematic study of experimental data. We evaluate the effectiveness and usefulness of Laboratory Forensics with laboratory members and data managers. Our preliminary evaluation indicates that LF is a useful approach for assessing data management practices. However, LF needs further developments to be integrated into the information systems of scientific laboratories.

Artificial intelligence (AI) is a growing research topic in national security due to the growing need for peaceful and inclusive societies, as well as for the maintenance of strong institutions of justice. As e-societies continue to evolve due to the advancements made in information and communication technologies (ICT), AI has proven crucial to guarantee the development of security measures, especially against growing cyberthreats and cyberattacks. This relevance has been translated into an explosive growth of AI applications for the improvement of decision support systems, expert systems, robotics, surveillance, and military operations that aim at ensuring national security. However, there is no bibliometric research on AI in national security, especially one that highlights current debates on the topic. This paper presents an overview of research on AI and national security, with emphasis on the research focus areas and debates central to research on the topic. We analyzed 94 references collected from the Web of Science (WoS) Core Collection and used VOS viewer software to analyze them. Based on these analyses, we identified 7 focus areas and 8 debates on AI in national security. We also identified the state and evolution of research on the topic in terms of main journals, authors, institutions, and countries. Our findings help researchers and practitioners better understand the state of the art of AI research on national security, and guides future research and development projects on the topic.

The value of Artificial Intelligence (AI) in augmenting or even replacing human decision-making in the organizational context is gaining momentum in the last few years. A growing number of organizations are now experimenting with different approaches to support and shape their operations. Nevertheless, there has been a disproportionate amount of attention on the potential and value that AI can deliver to private companies, with very limited empirical attention focusing on the private sector. The purpose of this research is to examine the current state of AI use in municipalities in Norway, what future aspirations are, as well as identify the challenges that exist in realizing them. To investigate these issues, we build on a survey study with respondents holding IT management positions in Norwegian municipalities. The results pinpoint to specific areas of AI applications that public bodies intend to invest in, as well as the most important challenges they face in making this transition.

Internet of Things (IoT) encompasses a wide range of devices and technologies which cumulatively shape a new environment with unprecedented business prospects. This paper aims to investigate the business potential of IoT, focusing on mobile IoT and IoT as a service. In this direction, it presents the case of RAWFIE, a H2020 project on a research and experimentation federated platform of mobile IoT testbeds and devices. The paper describes RAWFIE potential business models, analyzing them into their characteristics, based on the business model canvas, examining the possibilities as well as the challenges these entail, using a SWOT analysis, and testing them in a preliminary evaluation. The study offers research and practical insights as a starting point for business models of IoT as a service, focusing in the context of mobile IoT experimentation.

Due to rapid urbanization, city populations are rapidly increasing all over the world creating new problems and challenges. Solving some of these problems requires innovative approaches; one increasingly popular approach is to transform cities to Smart Cities. Smart Cities implement innovative approaches based on IoT technologies and convert many services to digital services. These services are implemented within the different components of a Smart City, helping city administrators to improve the life of the citizens, addressing different service, security and administrative challenges. The objective of this paper is to explore and determine how three well-known cities - Nice, Palo Alto and Stockholm - implemented the Smart City concept. The study indicates that a successful implementation of a Smart City model requires addressing a number of critical challenges: citizen involvement, business collaboration and strong leadership prove to be key success factors in the Smart City development process.

Recent technological advances have contributed to the development of smart homes, embedded with artificial intelligence, which aim to provide tailored services to residents. Smart home technologies benefit people daily and improve the environment in the long-term perspective. Despite the great interest of the research community in smart homes, the adoption rate is still low. The purpose of this study is to develop the research model, which can explain the acceptance of smart homes by users. Along with the relationship of technology acceptance factors with use behaviour, this study analyses the importance of individuals’ belief and values. Structural equational modelling has been employed to test the proposed hypotheses using a sample of 422 smart home users. The analysis revealed the significance of the relationships between values and perceived technology-fit, while the technology acceptance factors had a strong correlation with use behaviour leading to satisfaction.

Gamified persuasive system design refers to design solutions at the intersection of gamification and persuasive technology aiming at influencing attitude and behavior change. Although both concepts have been successfully applied in many domains to increase end-user engagement and satisfaction, results are often mixed and highly context specific. Consequently, there is a dearth of knowledge on how to design those solutions and how they are perceived by different types of users within certain application contexts. Thus, this paper investigates the relationship between the HEXAD gamification user types and persuasive principles within the context of energy saving. First results reveal (n = 206) that, three out of six persuasive principles (Reciprocity, Consistency & Commitment, Liking) have been perceived as persuasive by identified HEXAD user types, which highlights the importance of such user types models. Finally, this paper contributes to the present body of gamification literature by providing a human computer interaction (HCI) perspective which highlights guidelines for designing gamified persuasive systems to incentivize energy-saving.

Smart city innovations can enhance the quality of citizens’ life through different smart technology management solutions, including resource management, data management, and software management. Nowadays, there are two primary proposals for smart technology management architectures in smart city environments, centralized, and distributed-to-centralized. The distributed-to-centralized schema architecture has several advantages. These advantages emerge from the contribution of distributed (e.g., Fog and cloudlet), and centralized (e.g., Cloud) technologies. For instance, decreasing network communication traffic and its latencies, improving data quality, and upgrading the security and privacy levels. In this paper, we develop our proposed Distributed-to-Centralized Data Management (D2C-DM) architecture and suggest novel contributions. First, we describe a new fully hierarchical software management architecture for smart cities based on Fog, cloudlet, and Cloud technologies. This Distributed-to-Centralized Software Management (D2C-SM) architecture can provide different software and services layers (including local, historical, and combined) through using distinct data types gathered from physical (e.g., sensors and smartphones) and non-physical (e.g., simulated data, and external databases) data sources in the smart city. Second, we envisage that our proposed D2C-SM can fit the software requirements of the Zero Emission Neighborhoods (ZEN) center. Thereafter, we use three different use cases of the ZEN center to depict the easy adaptation of our proposed ICT architecture, including D2C-SM and D2C-DM architectures.

Social media plays an important role in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by b2b companies. The current study focuses on a number of aspects of social media such as: the effect of social media; social media tools; social media use; adoption of social media use and its barriers; social media strategies; and measuring the effectiveness of the use of social media. This research provides a valuable synthesis of the relevant literature on social media in the b2b context by analysing and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both academics and practitioners.

Technologies are changing how stakeholders, and investors access and capture data. Social Media has had a dramatic impact on how firms communicate with investors and stakeholders about their financial and sustainability reporting, giving them an edge over their competitors. The aim of this paper is to investigate the relationship between social media reporting and firm value of the GCC listed firms. To answer to research questions, the researchers collected cross-sectional data from a sample of 241 firms listed in the financial stock markets of the GCC for the year 2017. Additionally, an Index was used to calculate the total level of social media disclosure. The findings show that the 84% of firms in the GCC countries use social media, while 70% of these firms use SM for reporting. The results indicate that enhanced reporting levels through various social media channels significantly influence value of firms. These results have implications for GCC listed firms as it is important to examine how they can utilize social media to enhance their reporting process.

In times of increasing importance of social media services, we have to rethink information literacy. One of the key assumptions of existing information literacy constructs is “static” information, meaning that information does not change. But compared to traditional and mostly unidirectional media services such as printed newspapers or television, this does not reflect the reality of a social media context. Here, information can be characterized as “dynamic”, meaning that, for example, every user can easily modify information before sharing it (again). A measurement construct covering these novel aspects of information literacy is missing, so far. Thus, the main objective of this paper is to develop a rigor and updated construct to measure and quantify social media information literacy of an individual social media user. We selected a comprehensive construct development framework that guided us through the investigation, and which includes qualitative as well as quantitative analyses. The outcome is a theoretically grounded and empirically derived social media information literacy (SMIL) construct. The paper ends with a discussion of potential future research directions.

This study examined the relationship between work-related exhaustion and the recovery experiences of psychological detachment and relaxation during leisure, and the moderating role of emotion (positive & negative) when using Twitter during and after work. Participants were asked to rate their emotion based on the tweets they posted each day, together with their exhaustion at work and their recovery experiences at the end of the day. Results from the multilevel analyses showed that experiencing positive emotion when tweeting at work buffered the negative relationship of exhaustion and psychological detachment, but not relaxation. Negative emotion did not moderate the relationship significantly. The results show that social media can play a significant role in the recovery process and offer interesting insights both for employees and organisations.

Interactions in open source communities are often informal, and enacted through online discussion forums. While discussion and associated sentiment is critical to sustaining open source communities, they have not been studied to date. To address this gap in knowledge, this study uses sentiment analytics to illuminate the frequency of 2,364 discursive manifestations of contradictions through the theoretical lens of Activity Theory (AT). The study contributes to current discourse on contradictions by demonstrating the importance of dialectical contradictions as a driving force for learning, change, and sustaining open source communities. Implications for research and practice provide opportunities for revising current business methods and practices, which inevitably have implications for a sustainable society in the 21st century.

Nowadays, most of the organizations and businesses develop online services, which add value in their business and even increase their customer base. Social Media has changed the dynamics of digital marketing. Social media gives power to customers to post, share, and review content. Customers can directly interact with other customers and companies. In this paper, we will analysis huge user-generated content which can be used by organizations for their customer engagement strategies. The purpose of this study is to derive insights using Twitter Analytics on Twitter data to understand how businesses use Twitter for customer engagement strategies. Data collected from Twitter. The present paper uses descriptive and content analysis techniques for analyzing the tweets. The analysis will help in identifying the gaps in the priorities of the stakeholders. With the right customer engagement strategies, companies can make benefits.

Social media platforms are an inexpensive communication medium help to reach other users very quickly. The same benefit is also utilized by some mischievous users to post objectionable images and symbols to certain groups of people. This types of posts include cyber-aggression, cyberbullying, offensive content, and hate speech. In this work, we analyze images posted on online social media sites to hurt online users. In this research, we designed a deep learning based system to classify aggressive post from a non-aggressive post containing symbolic images. To show the effectiveness of our model, we created a dataset crawling images from Google search to query aggressive images. The validation shows promising results.

Ever since demonetization happened in India on November 8, 2016, there has been a steady improvement in the use of digital payments. The usage trend has become exponential in the last 15 months across the digital payment methods (DPMs) in India. Government of India is promoting this landmark change of Indian financial system through Digital India initiative. Both national and international corporates, established as well as startups are investing heavily in DPMs to promote their products and services among Indian consumers. The promotion of DPMs also includes the use of social media marketing. Social media (especially Twitter) is being extensively used by companies to make the consumer aware about their services and promote themselves. The consumers do get motivated to try and use their services but they also share their grudges on Twitter. This paper aims to analyze DPMs adoption in India using Twitter as a tool. This study collected 172996 tweets over a period of four months and analyzed using a mix of conventional as well novel social media analytics techniques. Our analysis highlights the critical factors that drive and inhibit the use of DPMs in India. Further, our state wise analysis clearly differentiates about DPM adoption rate from high to low in all states of India.

Governments around the world are using information and communication technologies to offer both simple information portals and transactional services. A less than one-third of the electronic government (e-government) initiatives focused on the provision of transactional services and understanding on studies related to the adoption of such services using domain-specific adoption theories/models are scarce. Therefore, the objective of this study is to understand the adoption of transactional service system, i.e. ‘Pak-Identity’ by employing a domain-specific model, i.e. Unified Model of Electronic Government Adoption (UMEGA). A UMEGA model with four new constructs is validated using data gathered from 441 citizens from all over Pakistan. A survey was conducted among citizens using simple random sampling technique. The collected data were analyzed employing variance-based structure equation modelling, i.e. partial least squares technique in SmartPLS 3.0 to test the formulated hypotheses. Findings indicate that (1) facilitating conditions is the predictor of effort expectancy, (2) performance expectancy, trust, and herd behaviour are the predictors of attitude, (3) price value, grievance redressal, and attitude are the predictors of behavioural intention to use e-government service. Surprisingly, effort expectancy, facilitating conditions, social influence, and perceived risk are found to be the nonsignificant predictors of adoption of e-government service. Interestingly, new constructs and new relationships are exposed, i.e. trust and herd behaviour on attitude, and price value and grievance redressal on behavioural intention. Moreover, a 55% variance in effort expectancy, 65% variance in attitude, and 40% variance in behavioural intention to adopt e-government has been found. Implications for the academics and managers are also outlined.

The innovative and improved delivery of public services is largely contingent on the co-creation process. Noting that engaging citizens in the development and delivery of e-government services is challenging and that limited attention is given to the process of facilitating citizen participation, this study intends to explore (a) governments’ perception of the co-creation; and (b) how governments can facilitate citizen participation in the development of e-government services . Through a meta-synthesis of qualitative case studies, this study identifies factors that are crucial for enabling co-creation, and develops a process view of the co-creation of e-government services to provide a holistic understanding on how the process of co-creation can be facilitated by the government and how the citizens could be engaged. The study thus contributes to the literature on e-government and public administration by improving the understanding of co-creation phenomenon, and suggests the mechanisms to improve citizen participation for the benefit of practitioners and policy makers.

Infrastructural development is a significant determinant of economic growth. It remains an elusive pursuit for many developing economies suffering from public infrastructural project failures. Although the causes of these failures are identifiable, they remain persistent. Government corruption has been identified as the primary cause of project failures amidst a host of other causal factors, spurred by the ambiguity in public service administration. These factors heighten capital expenditures and hence, the need for more transparent systems in public infrastructural project planning and -delivery. This research uses a case-study methodology to examine the importance of public involvement in addressing the causes of failures in public infrastructural project planning and -delivery. Using Nigeria as a case, the findings from conducted interviews and a document review support the proposition of a technologically collaborative approach in addressing the causes of public infrastructural project failures. The institutionalization of transparency-enhancing blockchain systems are vital in government and public involvement in the processes of public infrastructural project planning and -delivery.

This paper aims to investigate the process of personal data protection in Georgia within the frame of e-governance, focusing on available legal and technological protecting mechanisms, their practical usage and importance for realizing principles of good governance in the state. The scope of this research is defined by the protection of state databases containing citizen’s personal data. Its key legal and technological aspects are identified and analyzed. The potential of proper data protection to act as the enabler of e-governance services success is also evaluated. We explore the defense mechanisms of Georgian governmental entities by conducting interviews with seven experts from the Personal Data Protection Inspectorate and other public entities handling citizens’ data. We study citizens’ perception of data safety and the citizens’ knowledge of existing monitoring mechanisms through analysis of over 400 responses that we have received to our survey. We also analyze and assess the influence of these factors on the success of e-governance and its broad diffusion. Finally, guidelines and recommendations are formulated for raising citizens’ awareness on the data protection mechanisms to be used in future theoretical and practical considerations.

Identity is one of the basic building blocks of the Fourth Industrial Revolution, and as the capability of digital technologies improves drastically in the last decade, identity in digital form has become unavoidable. Identity entitles an individual to various services like voting, education, employment, insurance, healthcare etc. Yet there are around 1 billion people in the world at present that do not possess any form of official identity. Lack of identity has a significant impact on people living in rural areas, especially women, children, and financially backward families. In recently released Sustainable Development Goal-16 by the UN, it has been recommended that by 2030, every individual should be given a legal identity. India’s digital identity program –Aadhaar is one significant contribution in this direction considering its coverage. Rolling out a national identity scheme needs a considerable budget, time and most importantly, domain knowledge for smooth implementation. This paper attempts to identify the overarching goals of Aadhaar. The study also ranks goals based on their significance. The research uses focus group for data collection along with secondary data. The research in total identified nine primary goals with uniqueness, privacy and security as the high priority goals and scalability and future-proofing of technology as low priority goals. Total Interpretive Structural Modeling (TISM) has been used to identify the significance of each goal. This study could be taken as a starting point by other nations that are desirous of having a similar biometric identity program for its citizens.

This literature review focuses on the digital divide in contemporary technologically and economically advanced societies. Prior research shows that the digital divide entails more than physical accessibility and points to issues of technology acceptance and actual use. Recurring digital divide factors outside socioeconomic characteristics were identified in the articles reviewed. These factors relate to personality traits, motivation and digital skills. The factors can be used as the basis for a personality model for understanding acceptance and use of technology complementing models related to economic and social resources. Furthermore, measures for crossing the divide are traced in the literature and organized in three key intervention domains related to policy, training and design. The findings of this review can be a foundation for further research orienting researchers within the domain.

Autism, also known as autism spectrum disorder (ASD), is an incurable brain-based disorder that refers to a wide range of complex neurodevelopment disorders characterised by marked difficulties in communication and social skills, repetitive behaviour, highly focused interests and sensory sensitivity. Autism can present challenges for affected people at the work environment and in everyday life. The barrier for individuals with ASD increases further with changing environmental situations. Individuals with ASD have limited abilities to isolate their Five senses and often experience over- or under-sensitivity to sounds, touch, tastes, smells, light, colours or temperatures. In this perspective, individuals with autism may experience extraordinary challenges during a regular day for most people, especially in non-conductive crowded environments like workplaces.This work presents a survey of the state-of-the-art implementation as well as research challenges of assisting technology for people with ASD in the workplace and in everyday life. An overview of relevant key technologies and methods is outlined by focusing both on the therapeutic perspective as well as on the technological viewpoint. The aim of this paper is to provide a better understanding of the design challenges and to identify important research directions in this increasingly important area.

This study aims to explore the values of using smartphones in older adults’ daily lives in China. The value of using smartphones can be seen as the benefits associated with the use of smartphones in peoples’ daily activities. By employing the Value-Focused Thinking (VFT) approach, this study investigates what fundamental objectives and means objectives are important with the daily use of smartphones for older adults in China. Based on the data collected from the interviews, we developed a means-ends objective network describing the values of using smartphones for older adults in China. According to the results, maximizing well-being, maximizing life efficiency, maximizing safety, and maximizing digital inclusion are identified as the fundamental objectives to maximize values of using smartphones for older adults in China.

Information and communication technologies have become essential for everyday activities. Recently, ubiquitous or mobile computing is the new trend whereby information and services can be accessed anywhere and anytime. However, not all groups of society are taking advantage of its benefits, of which the older population is one of such groups. This study aims to identify and evaluate the factors that influence the adoption, use and diffusion of tablet devices within the older population (aged 50 and above) in UK. From an online survey of 203 completed responses, Compatibility, Perceived Usefulness, Perceived ease of use, Trust, Attitude and Perceived behavioral control were identified as factors that affect the adoption and use of tablet devices. This study also provides the implications of the research to academia, industry and policy makers.

This study pursued two objectives: (1) it comprehensively investigated the role of the factors facilitating social exchange, reciprocity expectation and social value in use behaviour, and (2) it examined the effect of the sharing economy on social inclusion and subjective well-being. The data were collected from 487 users of different sharing economy platforms in the United States. Structural equation modelling was employed to analyse the correlation of the examined variables. The results demonstrated the positive effect of egoistic belief, reciprocity norm, social value, and the negative effect of identification on the use of the sharing economy. In addition, strong relationships between use behaviour and outcomes were identified. Future research suggestions are provided.

E-Learning systems are receiving ever increasing attention in, academia, businesses as well as in public administrations. Managers and employee who need efficient forms of training as well as learning flow within the organization, do not have to gather in a place at the same time, or to travel far away for attending courses. Contemporary affordances of e-learning systems allow them to perform different jobs or tasks for training courses according to their own scheduling, as well as collaborate and share knowledge and experiences that results rich learning flow within the organization. The purpose of this article is to provide a systematic review of empirical studies in the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions Technology-Enhanced Organizational learning has been focused during the last decade. Future research should leverage on big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increasing organizational value.

While a number of advantages have been discussed on e-learning/e-assessment tools, little research has been reported on programming courses. Today, the different types of questions have been used in exams based on course type, e.g., Text-based questions, mathematical questions, and programming questions. All these question types require supporting plug-ins for e-assessments. In this study, we provide our practical experience on programming exams in Inspera Assessment and Blackboard Learn, especially focusing on Parsons problems (drag-and-drop questions) and code writing questions. Our findings indicate that currently, tools have basic support for programming exams, and also there is a low-level integration between the tools. However, the adaptability of any exam system could depend on the interoperability between the platforms and external plugins. Hence, more improvements can be made with the implementation of e-assessments in digital ecosystems while it requires a lot of changes internally and outside institutions. In the paper, we will explain how a digital ecosystem within e-assessment could improve assessments and how it supports diverse needs of programming exams.

The EU Commission for Higher Education through the Bologna Process declaration has put into action a series of reforms. One of the reforms is the development of learning outcomes in the European Credit Transfer and Accumulation System (ECTS). One part of these reforms requires European universities to identify and describe learning outcomes students will achieve after attending a course or program. There is no exact way of writing the learning outcomes, and there seems to be an indistinctly use of terms when explaining outcomes [1]. This can naturally cause some possible confusions in relation to what the learning outcomes should consist of, and make it difficult to write the learning outcomes for a course or a program. The purpose of this paper is to promote some explanations and clarifications that can ease the writing of the learning outcomes. The research question is How to write meaningful learning Outcomes? This paper presents a reflection on how the learning outcomes might be written. It is taken into account recommendations from relevant literature and framework intended for Europe. In this paper, the online course Applied Programming for Teachers is an example of how meaningful learning outcomes may be written with a focus on digital competence.

The paper deals with the process of acquiring professional vocabulary and grammar in English for Specific Purposes (ESP) in pre-service teacher preparation. The main objective of the research was to (1) discover how much students learn in the ICT-enhanced process of instruction, where autonomous work and collaborative learning are applied so that to build preconcepts and re-construct misconceptions in ESP; (2) detect the sources students exploited towards acquiring the learning content. The research was conducted at the Faculty of Education, University of Jan Evangelista Purkyne, Usti nad Labem, Czech Republic. Research sample consists of 62 probands enrolled in bachelor and master study programmes English Language and Literature and Teaching English Language and Literature. The research process is structured into three phases, and didactic tests are applied after each of them. Moreover, types of sources students used for acquiring the learning content were monitored and analyzed. Six hypotheses were tested and results showed statistically significant increase in knowledge after each phase.

While the need for the moderation of examination scripts as part of quality assurance is globally undisputable, moderation associated risks and challenges are more pronounced in developing countries. eModeration (online moderation of examination scripts) can improve the user experience of an examination moderation process while lowering the risk of losing scripts and delaying the moderation process. Various factors could contribute to resistance against implementing and adopting eModerate systems in Higher Education Institutions, ranging from human factors to technical issues and organisational resistance to change. The focus of this study is on the human factors involved in eModeration, i.e. the factors influencing the user experience when using eModeration. The research uses a design science research methodology, which includes the design, development and testing of a User Experience Evaluation Framework for eModeration. The contribution of this paper is the demonstration of how eModeration (e-Service) is relevant to Higher Education management, as well as the provision of some insights regarding eModerators’ user experience of an existing system. The resultant artifact is a validated User Experience Evaluation Framework for eModeration, which can be used to improve human experience of electronic moderation with an emphasis on improving the quality of educational assessment practices in developing countries.

We have experienced in the past few years massive use of various kinds of e-Learning methodology, such as m-learning and blended learning etc. Despite this ubiquitous implementation of these modern approaches, we still lack their proper use regarding their full exploitation in the virtual learning environment. M-learning platforms are ubiquitous, however, they lack the potential which is given to us from computational linguistics, deep learning, and artificial intelligence. The paper attempts to highlight this fact and brings this neglected topic to the attention of the creators and designers of these mobile apps because their potential is much greater and we should put our undivided attention to this issue if we want to improve the technology which we use.

Studies show that the integration of ICT in education is suffering from some barriers at student, teacher, and university level. This also holds for multi-campus education, as ICT is one of the main ingredients for sharing education among campuses. In this study, through a pragmatic case study at the context of a Scandinavian multi-campus university and with the help of activity theory, we examined the challenges of offering a single-campus course to students at multiple campuses by using ICT for lecture sharing, without further adjustments. Data for this study is collected from different sources (teachers, teaching assistants, and students) through interviews and observations and from an online questionnaire. Our findings suggest that repurposing a single-campus course to a multi-campus one without having clear and pre-defined rules poses some challenges for the different stockholders and influences their relations, resulting in negative impacts on teaching and learning processes and teacher and students’ satisfaction. Therefore, we conclude that there should be careful planning with a clear set of rules, including technology training and preparation for teaching staff, students, and technical staff. Staff structure may also need to be modified to accommodate the additional needs for technical support.

Teaching Assistants (TAs) are an indispensable part of higher education. TAs have two identities simultaneously: those of a student and of a teacher. However, the role of TAs’ may not be clearly defined, resulting in a role ambiguity. In this study, we have researched the TAs’ role in multi-campus education. Data for this research was collected through interviews with eight TAs at a Scandinavian multi-campus university who took a multi-campus TA role for the first time. This paper summarizes these TAs’ work tasks in multi-campus courses. Their tasks included setting up technology, dealing with technical problems, and communicating with other TAs in the course. Our study suggests that TAs’ challenges may be reduced if technical staff is caring for ICT equipment and technical problems, if direct interaction is provided between teacher and students, and if local teaching staff is involved in the activities at the remote campus.

Despite high quality information systems security in place, organizations are vulnerable to cyber-attacks due to lapses in the human behavior. The present paper explores the importance of human factors in cyber security using an online survey data. It uses the work of Parson, Calic, Pattenson, Butavicius, McCormac and Zwaans [23] in measuring the human aspects of cyber security (leaving printouts, links from known source, website access, information in website, password complexity, links from known source, plugging USB in public places) and their linkages with the demographic factors (age, work experience, academic discipline, qualification, and place). ANOVA was used on a sample size of 165. It was found that demographic profile of employees and students significantly differ in their perception towards the cyber security. The paper has suggestions for information security awareness training programmes to handle the inadequacies.

Technological advancements continue to disrupt how organizations compete and create value in almost every industry and society. The recent digital transformation movement has expanded the reliance of companies and organizations in software technologies, such as cloud computing, big data, artificial intelligence, internet-of-things, and also increase the risk associated with software usage. This work aims at identifying security risks associated with these technologies from an engineering management perspective. We conducted two focused groups and a literature review to gather and discuss the list of security risks. The findings have implications for both practitioners to manage software security risks and future research work.

This qualitative study focuses on how small- and medium-sized enterprises (SMEs) can realize benefits and create information technology (IT) value by investing in cloud enterprise resource planning (ERP) systems. We interviewed 19 respondents from cloud providers and cloud clients and found that their SMEs experienced both benefits and challenges when implementing cloud ERP systems. The digital value was obtained through work process automatization, fast updates of system functionalities, enhanced security of data storage, and increased access to critical business data from multiple digital units. Challenges in realizing these benefits related to organizational compliance with standard solutions and the need for organizational changes for employees to optimize system usage. The SMEs preferred an informal process for realizing benefits and creating digital value from the system. In contrast, the providers wanted to integrate benefits realization as part of their formal implementation methodology. Based on frameworks identified in the literature, we integrate a benefits realization model with an information systems value model to understand how SMEs realize benefits and create business value from cloud ERP systems. We contribute to the SME literature and explain the value creation process for SMEs implementing cloud ERP systems.

Globalisation and technology advancements have disrupted the organisational landscape and with the proliferation of new technology; risk management is fundamental to transforming the business especially considering the dynamic nature of the digital society organisations now exist in. However, the challenge faced by the enterprise risk management (ERM) function operating in such a dynamic and transformative environment, is the capability to continuously innovate, evolve and transform its risk management processes to meet the needs of the organisation. Questionnaire survey research examined the relative importance of 18 critical success factors for dynamic ERM. Factor analysis revealed that the appropriate grouping of the 18 critical success factors (CSFs) are ERM charter, ERM processes, and ERM business alignment. These findings should empower organisations to identify risk management processes influencing agility in the risk management practise applied.

German enterprises are often characterised by low levels of digital maturity. One reason for this is a lack of required structural changes on the path towards digital transformation. We consider a prescriptive framework, the digital transformation framework (DTF), which contains four structural requirements for digital transformation. Based on 16 interviews with German digitalisation experts, we aim at an evaluation of the DTF. The outcome is an enriched version of it containing seven structural requirements including the newly identified factors culture of change, agility of organizational structure, and integration of cloud computing and platforms. The extended DTF sheds light on additional facets of the digital transformation and thus supports managers in navigating their undertaking in this dynamic environment. Corresponding implications for research and practice are discussed.

With the popularity of digital transformation programmes becoming widerspread, our study is motivated by a need to understand who has the responsibility for digital transformation across different types of organisations and industries. We take a specific focus on the Chief Digital Officer which has been considered the most fast growing executive role in the recent years. In particular, in this paper, we present a research in progress study that aims to examine the role of Chief Digital Officer in enabling digital transformation in traditional, pre-digital organisations, and to identify its impact on the IT department of these organisations. The empirical study is based on the qualitative approach with its main dataset deriving from a series of semi-structured interviews. The study is expected to have both theoretical and practical implications on the management of digital transformation.

For several decades, maturity models have been regarded as a magic bullet for enterprises’ economic growth processes. In these models, domains are structured and divided into (mostly linear) levels that are used as benchmarks for enterprise development. However, this approach has its shortcomings in the context of complex topics like digitalization. As we understand it, digitalization defines a conceptual approach to a phenomenon that is individually important and promising in different ways for different enterprises. Consequently, existing maturity models in their current form are not able to reproduce the full extent of digitalization for enterprises, as the models are too general—especially for SMEs of different sizes and from different sectors. In our research, we propose a maturity model approach that introduces the concept of roles as a possibility to depict enterprises’ specific components, which are then added to a static core model. By doing so, the resulting maturity model is more flexible and scalable for SMEs’ specific needs. Furthermore, we introduce a new assessment approach for defining whether improving digitalization is truly relevant and worthwhile for the enterprise.

Open innovation ecosystems rely upon inter-organisational knowledge transfer to support co-creation. Despite the significance of this process, and an abundance of open innovation research, empirical investigation and discussion of diverse knowledge transfer conditions across open innovation ecosystems remains unaddressed within existing literature. Using a mixed-method approach, this study investigates how knowledge, firm, and partner-relationship characteristics affect the successful exchange of knowledge between ecosystem partners. Interpretive Structural Modelling was employed to ascertain expert opinions regarding the interrelations between the transfer conditions. The combinatory nature of these conditions, and their integration into solutions for success, was further explored utilizing fuzzy-set Qualitative Comparative Analysis. Results indicate that conditions for knowledge transfer success are highly interrelated and co-dependent. Limitations and implications are discussed.

Digital disruption is the phenomenon when established businesses succumb to new business models that exploit emerging technologies. Futurists often make dire predictions when discussing the impact of digital disruption, for instance that 40% of the Fortune 500 companies will disappear within the next decade. The digital disruption phenomenon was already studied two decades ago when Clayton Christensen developed a Theory of Disruptive Innovation, which is a popular theory for describing and explaining disruption due to technology developments that had occurred in the past. However it is still problematic to understand what is necessary to avoid disruption, especially within the context of a sustainable society in the 21st century. A key aspect we identified is the behavior of non-mainstream customers of an emerging technology, which is difficult to predict, especially when an organization is operating in an existing solution space. In this position paper we propose complementing the Theory of Disruptive Innovation with design thinking in order to identify the performance attributes that encourage the unpredictable and unforeseen customer behavior that is a cause for disruption. We employ case-based scenario analysis of higher education as evaluation mechanism for our extended disruptive innovation theory. Our position is that a better understanding of the implicit and unpredictable customer behavior that cause disruption due to additional performance attributes (using design thinking) could assist organizations to pre-empt digital disruption and adapt to support the additional functionality.

There are few studies conducted on personal processes within the Business Process Management (BPM) domain. Personal processes are looser and more context- and person-dependent compared to the clearly defined business processes. This makes it more challenging to create solutions in this domain. In this study, a taxonomy is developed for personal processes. We used the data collected from semi-structured interviews that we have conducted with a diverse population. We built a taxonomy with 4 classes and 22 subclasses, further organized by 6 characteristics and 3 dimensions. The proposed taxonomy is intended to guide practitioners and researchers by identifying the range of processes, by understanding the relationship among process types, and by organizing the knowledge within the Personal Process Management (PPM) domain. As such, our work would lead to creating new methods, tools, and approaches for increased effectiveness of PPM solutions.

In this paper we present a virtual collaborative student project across two universities in the UK and Norway. The students involved were all Master students in Digital Innovation & Analytics and Digital Collaboration retrospectively. The project also had an industry partner, Cisco, and was set up as part of students’ learning in their corresponding universities. Five student-based Global virtual teams (GVTs) were formed. We draw on the experiences of these GVTs to gain better understanding of students’ experiences in dispersed collaboration giving particular focus on the leadership practices adopted in student-based GVTs. Our analysis of the GVT members’ experiences and reflections show that the way leadership was enacted had a role to play in the collaboration within the GVT. Overall, students appreciated being given the opportunity to be part of a globally dispersed project and were able to identify lessons learned and skills that they gained from the experience. Further, students were able to get practical experience in being part of virtual teams and to implement some of the ideas and approaches that they have learned from theory and in class discussions. For example, personal qualities that are central to success in virtual teams, i.e. communication skills, intercultural skills, interpersonal skills, methodological and technical skills, team working skills and leadership skills.

Crime is a reality that effects everyone in the world. Even developed countries such as the United Kingdom, Canada and Germany are not exempted from crime occurrences. Although these indicators are substantially less than developing countries such as South Africa, the existence of crime is a worldwide phenomenon. In this paper we explore the extent to which social media, in particular Facebook are used in the fight against crime.The study adopts a social technical approach in its investigation, considering the symbiotic relationship between communities (the organisation), Facebook and the utilisation of Facebook to complete tasks (technical subsystem), team members and structure to report crime in virtual communities (social subsystem) and current governance structures (environmental system). Based on a study of 297 crime fighting Facebook communities in South Africa, we found a positive correlation between the number of Facebook crime fighting communities per region and the crime rates for a particular region. Furthermore, we noticed that the regions with the most crime communities also had the most Internet connectivity per household. Both findings are indicative of a functional symbiotic relationship between the technical subsystem and the social subsystem. However, it highlights the fact that these structures are initiated by communities therefore lacking strong intervention from the environmental system, in this instance governmental bodies. We propose that governmental agencies formally recognise social media platforms as social crime fighting tool. Secondly, we suggest that governmental entities should focus on infrastructure related challenges as part of their attempt to combat crime.