Samuel Anim-Yeboah

Richard Boateng
email: richboateng@ug.edu.gh

Emmanuel Awuni Ko

Acheampong Owusu

Ibrahim Bedi

Digital Entrepreneurship in Business Enterprises: A Systematic Review

Keywords: Digital Technology, Digital Entrepreneurship, Business Enterprise, Systematic Review

This study systematically reviews extant contemporary literature on digital entrepreneurship in peer-reviewed journal articles over six years (2013)(2014)(2015)(2016)(2017)(2018) from six journal databases. It involved a systematic literature review of 101 papers from 53 journals focusing on the publication outlets, yearly trends, themes, and associated theoretical and conceptual approaches, methodologies, sources and geographical distribution of digital entrepreneurship research. The findings suggest that extant literature mostly lacked sound theoretical underpinnings. More work adopting appropriate and proven theoretical approaches is needed. Most of the reviewed papers also focused mainly on issues relating to the technology itself than those relating to the enterprise or the entrepreneur. The capabilities and capacities of enterprises, as well as the strategies in implementing digital technologies and harnessing the opportunities of digitalization, are key issues that have not hitherto received much attention. The study contributes to the understanding of the conceptualization of the digital entrepreneurship phenomenon. Future research should consolidate the understanding of the field, with models and frameworks that recognize digital entrepreneurship as an academic research field in its own right, and also consider the impact of enterprise capabilities and capacities on digital entrepreneurship.

Introduction

Digital entrepreneurship is generally defined as the pursuit of business or economic opportunities based on the use of digital technologies [START_REF] Davidson | Digital entrepreneurship and its socio-material enactment[END_REF], and this definition is adopted for this study. The entrepreneurs involved in the digital entrepreneurship are then described as digital entrepreneurs while the resulting ventures or firms, which provide economic and social value for themselves or their communities, are referred to as digital enterprises [START_REF] Davidson | Digital entrepreneurship and its socio-material enactment[END_REF][START_REF] Zhao | Digital entrepreneurship: Research and practice[END_REF]. Although researchers and policymakers have widely used the term 'digital entrepreneurship', its conceptualisation remains elusive, with very little evidence of scholarship in the field [START_REF] Zhao | Digital entrepreneurship: Research and practice[END_REF].

There is a growing interest in digital entrepreneurship since it is considered to be the ultimate and contemporary trend in entrepreneurship development due to the rapid development of digital technologies and the emerging digital economy [START_REF] Hafezieh | Exploration of process and competitive factors of entrepreneurship in digital space: A multiple case study in Iran[END_REF]. Bogdanowicz [START_REF] Bogdanowicz | Digital Entrepreneurship Barriers and Drivers-The need for a specific measurement framework[END_REF] also emphasises the renewed and increasing interest in digital entrepreneurship and calls for empirical evidence [START_REF] Hull | Taking advantage of digital opportunities: a typology of digital entrepreneurship[END_REF].

Despite the increased interest in digital entrepreneurship and technology-based innovations, there has been limited clarification of the concept from different perspectives and conceptualisations. Moreover, there has also been a lack of contextual and conceptual development and discussion of the concept of digital entrepreneurship, as most prior research examined only the sporadic phenomena associated with it [START_REF] Shen | Can the entrepreneurship course improve the entrepreneurial intentions of students?[END_REF][START_REF] Zhao | Digital entrepreneurship: Research and practice[END_REF]. Furthermore, some critical and fundamental issues about digital entrepreneurship currently remain unresolved in the literature. These include how digital technologies transform entrepreneurship, how digital entrepreneurship predicts performance outcomes, and how digital entrepreneurship differs from traditional entrepreneurship [START_REF] Yunis | The role of ICT and innovation in enhancing organizational performance: The catalyzing effect of corporate entrepreneurship[END_REF][START_REF] Zhao | Digital entrepreneurship: Research and practice[END_REF]. Meanwhile, not much has been done in terms of reviewing the body of literature and research trends in digital entrepreneurship, so the need for conceptualisations in the field is much desired [START_REF] Nambisan | Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship[END_REF].

Furthermore, there is a dearth in knowledge regarding the detailed classification of digital technology-enabled entrepreneurship and enterprises, making it difficult to appreciate the current level of understanding and boundaries of the original concept [START_REF] Ngoasong | Impact investing and inclusive business development in Africa: a research agenda[END_REF][START_REF] Shemi | E-commerce and entrepreneurship in SMEs: the case of myBot[END_REF]. Additionally, the current conceptualisation of digital entrepreneurship is considerably diverse. While some researchers have opted for a broad conceptualisation of digital entrepreneurship as a combination of digital technology and entrepreneurship innovation [START_REF] Beckman | The role of the entrepreneur in technology entrepreneurship[END_REF][START_REF] Ferreira | What Do We [Not] Know About Technology Entrepreneurship Research?[END_REF], others have limited the concept to the attainment of entrepreneurship goals with digital technological applications [START_REF] Wallin | Entrepreneurial Growth Ambitions: The Case of Finnish Technology Start-ups[END_REF].

It is essential to review achievements and studies to date, regarding what has been done, what needs to be revisited and what is still missing in the field, in order to better appreciate and promote the development of digital entrepreneurship on the academic and research front [START_REF] Mosey | Technology entrepreneurship research opportunities: insights from across Europe[END_REF]. A review of studies on the concept of digital entrepreneurship is necessary to evaluate the current understanding of, and complementary perspectives on, how the digital technology revolution has permeated entrepreneurship and innovation [START_REF] Hanna | We're all connected: The power of the social media ecosystem[END_REF].

Hence, compelled by the challenges posed by the development of the digital entrepreneurship concept, particularly in the IS research environment, this study seeks to provide a systematic review of the extant literature on digital entrepreneurship. The study will identify and describe the major issues, themes, trends, distribution, and focus of research on the concept. It will also examine the methodological and theoretical approaches to past studies on the concept, identify the limitations and gaps in the literature, and offer recommendations for future research. The resulting review is expected to serve as a one-stop source, offering insight into what has been accomplished so far, what is currently being done, and what challenges and opportunities lie ahead, in terms of research on digital entrepreneurship. The study, therefore, addresses the following questions to achieve this:

1. What are the major trends, characteristics, and distribution of research work on digital entrepreneurship? 2. What major issues and themes are being focused on and discussed in digital entrepreneurship research?

3. What theoretical, conceptual, and methodological, approaches are being used to address digital entrepreneurship research? 4. What are the limitations and gaps in the extant literature on digital entrepreneurship? This study seeks to systematically review research articles concerning digital entrepreneurship in peer-reviewed journals from six major journal databases, over six years. The next section presents the methodology employed, while the third section presents the findings and discussions. The fourth section elaborates the limitations and gaps identified, followed by the conclusion and contribution.

Methodology

The study was conducted as a systematic literature review (SLR) of extant studies on the conceptualisation of digital entrepreneurship [START_REF] Okoli | A Guide to Conducting a Standalone Systematic Literature Review[END_REF]. The searches for articles were conducted in six electronic databases for which the researcher had full-text access: Sci-enceDirect/Elsevier, Emerald, AIS Library, Sage, Springer, and Taylor and Francis. Although these databases may not exhaustively list all relevant journals, they, however, cover a reasonable portion of the existing database for IS journals. As Levy and Ellis, [START_REF] Levy | A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research[END_REF] noted in their guide to a systematic approach to a literature review in IS, it is better to use multiple databases in conducting literature searches, since the IS domain is multidisciplinary and IS literature outlets are highly diversified. Quality IS literature is dispersed through-out hundreds of databases and some of the databases used in this study, being multidisciplinary, are among those recommended by Levy and Ellis, [START_REF] Levy | A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research[END_REF] as useful for IS research. Moreover, most journals in these databases are globally top-ranked IS journals [START_REF] Cabs | Academic Journal Guide (AJG)[END_REF]. Against this backdrop, the list of databases above was a fair and adequate representation of the relevant IS databases suited for digital entrepreneurship study, which is a multidisciplinary subject. The searches were conducted using "digital innovation" and "digital entrepreneurship" as search terms. Other keywords included "digital enterprise," "digital economies," "digital technologies," and "innovative technologies." The search was limited to articles published between January 2013 and August 2018, resulting in 175 papers or articles, that were downloaded.

The exclusion criteria applied included the delimiting of the papers to peer-reviewed research articles, and hence, conference papers and book chapters were excluded from the study, in addition to stock reviews. The articles were also restricted to those concerning business entrepreneurship and business enterprises, and thus, all articles concerning policy, education, and social entrepreneurship were eliminated. After the papers had been identified and elicited, they were sorted and cross-checked to eliminate duplications.

Ultimately, 101 articles from 53 journals were selected. The selected papers were then classified based on the publishing journal, year of publication, digital technology issues and themes, theoretical and conceptual approaches and frameworks, research methodologies and methods, data sources and levels of analysis. The data collected on the various classifications were analysed and summarized using descriptive statistics.

3

Findings and Discussions

Publication Databases and Journals

Regarding the distribution of the articles in the databases, it was found that Emerald hosted the majority of the publications, followed by ScienceDirect, Springer Link, and Taylor and Francis. Sage and AIS Library, by contrast, had small numbers of journals and articles, with particularly the AIS Library having the least. Such observation may be explained by the fact that, because digital entrepreneurship researches straddle multidisciplinary fields, it may be expected that libraries and databases that accommodate multidisciplinary fields will have more articles on digital entrepreneurship [START_REF] Levy | A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research[END_REF]. Moreover, apart from the AIS Library, which contains papers specifically related to IS, the other libraries (Emerald, ScienceDirect, Taylor and Francis, Springer Link and Sage) contain papers from several different fields [START_REF] Levy | A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research[END_REF].

In terms of the number of articles per journal, the Journal of Small Business and Enterprise Development had the highest number of 11 (10.9%) articles and is followed by Technological Forecasting and Social Change with 9 (8.9%) articles. The Information and Management journal had 5 (4.9%) articles, Journal of Business Research and the Journal of Information Technology had 4 (4.0%) articles each, while the Journal for Innovation and Entrepreneurship, Journal of Strategic Marketing, Small Business Economics journal and the Journal of Open Innovation, Technology, Market and Complexity had 3 (3.0%) articles each. Twelve [START_REF] Dutot | From strategic orientation to social media orientation: Improving SMEs' performance on social media[END_REF] of the journals had 2 (2.0%) articles each while the rest of the journals, thirty-two (32) in all, had 1 (1.0%) article each.

The focal areas for many of the journals from which the papers were obtained included information systems (IS), information technology (IT), innovation, business, entrepreneurship, management, marketing, human relations, governance, regulation, operation, production, knowledge, planning, strategy, gender, and other diverse fields. These focal areas suggest the suitability of the journals for digital entrepreneurship research, which is a multidisciplinary concept that is applied in different scientific and academic fields. It further corroborates the observation that digital entrepreneurship is multidisciplinary in perspective [START_REF] Carlborg | The evolution of service innovation research: a critical review and synthesis[END_REF][START_REF] Whittington | Digital Innovation and Entrepreneurship[END_REF][START_REF] Yoo | Research commentary-the new organizing logic of digital innovation: an agenda for information systems research[END_REF].

Year of Publication

The distribution of the publications by year (Figure 2) shows an increasing trend of articles on digital entrepreneurship from January 2013 to August 2018. Throughout the period under study, the number of papers published continually increased from six papers in 2013 to 33 papers in 2018 (up to August only), representing more than five-fold increment. Apart from an insignificant drop from six in 2013 to five in 2014, the increment was consistent, as the number of papers increased to 13 in 2015, 14 in 2016, 30 in 2017, and 33 in 2018. This trend shows that the number of publications is likely to increase further in the future. This trend shows a growing interest in digital entrepreneurship, not only in practice and policy but also in research, which affirms Kelestyn and Henfridsson's [START_REF] Kelestyn | Everyday digital entrepreneurship: The inception, shifts, and scaling of future-shaping practices[END_REF] claim. Considering the proliferation of digital technology and information technology (IT) based business, the increase in digital entrepreneurship research is expected to continue for several years [START_REF] Giones | Digital technology entrepreneurship: A definition and research agenda[END_REF][START_REF] Kelestyn | Everyday digital entrepreneurship: The inception, shifts, and scaling of future-shaping practices[END_REF][START_REF] Miniaoui | Innovation and Entrepreneurship for the growth and diversification of the GCC Economies[END_REF].

Focus and Categories of Research Issues and Themes

The study identified several issues of focus in the papers reviewed. These issues were grouped into four categories as (i) those that focused directly on the technology involved (73 papers), (ii) those that focused on the relationships and interactions with the technology (58 papers) (iii) those that focused directly on the enterprise (53 papers), and (iv) those that focused on the entrepreneur (13 papers).

The technology-focused issues concerned digital technologies like mobile technology, e-business platforms, social media, cloud computing, big data, crowdsourcing, internet, enterprise systems, and blockchain [START_REF] Zalan | Born global on blockchain[END_REF]. Some of the technology-focused articles, however, did not indicate any specific technologies but mentions digital technologies or ICT in general. The interaction-focused issues comprised access, adoption, impact, role, influence, possession, trust, and use of digital technology. The enterprisefocused issues involved demographics, business model, innovation, transformation, performance, productivity, profitability, value, expansion, growth, convergence, ecosystems, incubations, start-ups, cooperation, competition, internationalisation, marketing, stakeholder collaboration, success factors, and strategic orientation. The entrepreneur-focused issues encompassed behaviour, gender, competence, perception, positions, and management.

Given that digital entrepreneurship is ICT-driven [START_REF] Ngoasong | Digital entrepreneurship in a resource-scarce context: A focus on entrepreneurial digital competencies[END_REF], it was not surprising that the technology-focused issues (from 73 papers) dominated the various issues addressed. The distribution of the themes within the technology focussed issues shows that most of the papers, 33 articles (32.7%) did not specify the exact technology theme. Specifically, the dominant technology theme identified from the publications on digital entrepreneurship included e-business platforms with 12 (11.9%) papers and social media platforms with 10 (9.9%) papers. Other digital platforms and mobile technology reflected in 4 (4.0%) papers each while cloud computing had 3 (

Theoretical and Conceptual Approaches

Regarding the use of specific theories or concepts, the findings show that 57 (56.4%) of the papers had no theory or concept underpinning it, while 44 (43.6%) had theories or frameworks. Of the 44 papers that were underpinned by theories or concepts, 35 (34.6%) used single theories, while 9 (8.9%) combined two or three theories. Meanwhile, 14 (13.9%) of the 35 papers that used single theory or concept and 1 (1.0%) of the nine papers that combined theories or concepts utilized the author's frameworks. This implies that only 29 (28.7%) of the papers, comprising 21 (20.8%) of the 35 papers with single theory and 8 (7.9%) of the nine papers with combined theories, utilized known and established theories or concepts. In all 28 different known and established theories and concepts were employed. The Dynamic Capability theory was used in 4 papers, while the Resource-Based View was used in 3 papers. Diffusion of Innovation, Institutional Theory, Technology Acceptance Model, Technology Organization and Environment framework, Theory of Planned Behavior, and the Trust Theory were used in two papers each, while each of the remaining 20 theories was used in one paper each. The theory-based studies focused on enterprise-related issues such as business model, value, process, innovation, and transformation, as well as competition, expansion, marketing, and strategic orientation. The review also shows that some of the studies with no theories could have been underpinned with applicable theories in IS literature such as social theories, socio-technical theories, institutional theories and the Task-Technology Fit (TTF) theoretical framework which could explain the assumptions on which many of the publications were conducted [START_REF] Chandna | Peer-to-peer selling in online platforms: A salient business model for virtual entrepreneurship[END_REF][START_REF] Panayiotou | Micro firms internet adoption patterns: the case of the Greek jewellery industry[END_REF][START_REF] Tumbas | The 'digital facade' of rapidly growing entrepreneurial organizations[END_REF].

These theories and concepts that were used by the papers reflect the wide application of different theories and concepts in digital entrepreneurship for different purposes, depending on the focus of the research, which emphasizes the multidisciplinary nature of digital entrepreneurship with a diversity of research approaches. With fewer papers, 29 (28.7%) in all, utilizing 28 different established theories and concepts also suggests the newness of the knowledge area in research. For studies in digital entrepreneurship to gain prominence in the IS research landscape, further work based on grounded, appropriate, and credible theoretical approaches should be considered.

Among the theories used, the most dominant was the social theories approach with 25 (24.8%) papers, followed by the socio-technical theories approach with 11 (10.9%) papers and the technical theories approach with 5 (4.9%) papers. The dominance of social theories could be due to the social nature of entrepreneurship studies. Many of the studies that used social theories focused on ICT adoption and impact [START_REF] Durkin | Exploring social media adoption in small to medium-sized enterprises in Ireland[END_REF][START_REF] George Wynn | E-business and process change: two case studies (towards an assessment framework)[END_REF][START_REF] Sarmah | Antecedents of e-business adoption intention: an empirical study[END_REF], while others used the theories to explain the influence and impact of ICT on business [START_REF] Daniel | Effectuation and home-based online business entrepreneurs[END_REF][START_REF] Dutot | From strategic orientation to social media orientation: Improving SMEs' performance on social media[END_REF][START_REF] Sedera | Innovating with enterprise systems and digital platforms: A contingent resource-based theory view[END_REF]. From the review, it also emerged that studies that adopted the socio-technical theories approach focused on the influence of ICT in business [START_REF] Chandna | Peer-to-peer selling in online platforms: A salient business model for virtual entrepreneurship[END_REF], the extent of ICT adoption, and barriers to its adoption [START_REF] Panayiotou | Micro firms internet adoption patterns: the case of the Greek jewellery industry[END_REF]. The theories applied in the publications reviewed are popular and prominent in IS research.

Research Methodology Used and Trend

Regarding the classification of the publications by the research methodology employed, four distinct groups emerged, namely, those that used mixed methods, qualitative methodology, quantitative methodology, and those with no defined methodology. Regarding the distribution, the results show the almost equal distribution for studies using qualitative (41 (40.6%) papers) and quantitative methodologies (39 (38.6%) papers), which were the dominant approaches. Just a few papers used mixed methods 3 (3.0%), while a reasonable number did not use any defined methodology 18 (17.8%).

Of the 41 quantitative papers, 22 (21.8%) were based on theories, of which 15 (14.9%) were established theories. Whereas, of the 39 qualitative papers, 17 (16.8%) were based on theories, of which 12 (11.9%) were established theories. Having 83 (82.2%) of the papers with both quantitative and qualitative methodologies almost equally shared suggest that the digital entrepreneurship research is becoming more mature with proven methodologies. The 18 studies that were not underpinned by any defined methodology were made of 16 reviews, and two content analysis papers and most did not have any theory as well. Moreover, with 13 of the 18 no defined methodology and review papers published in 2017 and 2018, it goes to suggest the growing interest in exploring the research already done in the area of digital entrepreneurship. It was further observed that both quantitative and qualitative methodologies have increased in use in recent years (Figure 2). The increasing use of qualitative methodology suggests the subjection of digital entrepreneurship to exploratory research, being a new field, while the increasing dominance of quantitative methodology also reveals the simultaneous development of statistical rigor and analysis [START_REF] Haghighi | Gap analysis of current and desired states of entrepreneurship development components in the field of ICTs Iran[END_REF][START_REF] Sarmah | Antecedents of e-business adoption intention: an empirical study[END_REF]. According to Creswell [START_REF] Creswell | Research design: Qualitative, quantitative, and mixed methods approaches[END_REF], qualitative methods are commonly used in fields that are new and require more exploratory research designs, hence the observed trend reflects the attractiveness of digital entrepreneurship as a new area of research. The data sources used in the publications included secondary sources, primary sources, and some undefined sources. The results indicate that about two-thirds, 68 (67.3%) of the papers used data from primary sources, whereas about a quarter, 27 (26.7%) of the papers used secondary data sources. The data sources available for a study largely depend on the availability of previous studies on the topic and the research approach employed. The survey and case study research approaches tend to employ primary sources [START_REF] Dutot | From strategic orientation to social media orientation: Improving SMEs' performance on social media[END_REF][START_REF] Mohajerani | Exploring the role of social media in importing logics across social contexts: The case of IT SMEs in Iran[END_REF][START_REF] Moghavvemi | Entrepreneurs adoption of information system innovation: The impact of individual perception and exogenous factors on entrepreneur's behavior[END_REF], whereas most reviews tend to rely on secondary sources [START_REF] David-West | Platforms in Sub-Saharan Africa: start-up models and the role of business incubation[END_REF][START_REF] Xiao | ICT innovation in emerging economies: a review of the existing literature and a framework for future research[END_REF]. Based on the results of the study, survey and case study were the two dominant methods used, which resulted in the use of primary data sources for many of the papers. Meanwhile, the data sources and methods used are also influenced by the level of research development in the field of inquiry [START_REF] Ireland | Entrepreneurship research in AMJ: What has been published, and what might the future hold?[END_REF]. In relatively new areas of research, obtaining secondary data or the adoption of a literature review approach may be hampered. The limited use of secondary data sources in many of the works published in this area reflects the relative newness of the field. This assertion is also reflected in the research approach that was employed in the study. From the results, it is clear that the most widely used methods were a survey, case study, literature review, and interviews. The use of the case studies and interviews suggest that more exploratory and descriptive questions are being asked at the same time that rigor is being sought through surveys. The greater use of the research approaches mentioned above, compared to others, suggests that many of the papers were focused on discovering and describing phenomena related to digital entrepreneurship [START_REF] Ireland | Entrepreneurship research in AMJ: What has been published, and what might the future hold?[END_REF]. Interestingly of the 35 papers that used a case study or interview methods, 21 of them have no theory base, which further emphasizes the exploratory and descriptive purposes. The focus of such papers included adoption, impact, influence and use of digital technologies like social media, e-business, enterprise systems and crowdsourcing [START_REF] Foroudi | Digital technology and marketing management capability: achieving growth in SMEs[END_REF][START_REF] Kuester | Get the show on the road: Go-to-market strategies for e-innovations of start-ups[END_REF].

Research methods, approaches, and data sources used may also be entirely dependent on the level of analysis that the researcher intends to perform [START_REF] Bryman | Business research methods[END_REF]. The results show that many of the studies were conducted at the micro-level, with very few at the macroand meso-levels, which suits entrepreneurship research [START_REF] Ireland | Entrepreneurship research in AMJ: What has been published, and what might the future hold?[END_REF]. The current focus on organizational level noted in the study may be explained by the fact that many of the studies are applied studies that do not simply seek to advance knowledge, but also to understand, contextualize and explore the practices of entrepreneurs and SMEs that have adopted digital innovation technologies [START_REF] Ensign | Serial entrepreneurs in the Waterloo ecosystem[END_REF][START_REF] Fu | Innovation and productivity in formal and informal firms in Ghana[END_REF][START_REF] Garcia-Murillo | ICTs and the informal economy: mobile and broadband roles[END_REF][START_REF] Hartmann | Capturing value from big data-a taxonomy of data-driven business models used by start-up firms[END_REF][START_REF] Kabongo | ICT possession among Congolese SMEs: an exploratory study[END_REF][START_REF] Panayiotou | Micro firms internet adoption patterns: the case of the Greek jewellery industry[END_REF].

Limitations, Gaps and Future Research

The main limitation of the study was the restriction of the electronic databases to only six, namely AIS Library, Emerald, Sage, ScienceDirect/Elsevier, Springer Link, and Taylor and Francis Online. Another limitation is the restriction of the study to articles published between 2013 and 2018. Such limitations would result in some appropriate articles being eluded. Future research could expand on the database and also consider other forms of digital entrepreneurship. Only a few of the studies discussed had sound theoretical underpinnings. A major gap identified, is the limited use of theoretical and conceptual frameworks that would bring the concept of digital entrepreneurship up to par with major areas of academic inquiry in IS research. Meanwhile, for studies in digital entrepreneurship to acquire some eminence in the IS research purview, more work adopting appropriate and proven theoretical approaches is needed. Few papers focused on issues relating to the enterprise while much fewer papers focused on the entrepreneur. Future research should consider the drivers and motivation of the entrepreneur for digital entrepreneurship. The capabilities and capacities of enterprises, as well as the strategies in implementing digital technologies and harnessing the opportunities of digitalization, are key issues that have not hitherto received much attention.

Future research should consolidate the understanding of the field, with models and frameworks that recognize digital entrepreneurship as an academic research field in its own right, and also consider the impact of enterprise capabilities and capacities on digital entrepreneurship

Conclusion and Contribution

There is an arguable case for acknowledging digital entrepreneurship as a distinct field that has attracted considerable scholarly attention in recent years. As a multidisciplinary and multi-sectoral subject, research on digital entrepreneurship encompasses many fields. Owing to the broad range of research within the scope of digital entrepreneurship, the understanding of the concept is diffuse and open to misinterpretation.

The paper provides guidance for researchers with insight into the conceptualization of digital entrepreneurship as a multidisciplinary research field. It will also help academicians understanding a holistic view of available research and the developing trend in digital entrepreneurship. This paper contributes to information systems research by describing and classifying the published literature in digital entrepreneurship and by pointing out the gaps where further research is most needed. Furthermore, the paper provides a framework that may provide a conceptual structure for future studies in digital entrepreneurship.

Fig. 2 .

 2 Fig. 2. Trends in the publication by year (N = 101)

Fig. 2 .

 2 Fig. 2. The yearly trend of research methodology


Phaphama Kangelani1
email: phaphama3@gmail.com

Tiko Iyamu

A model for evaluating big data analytics tools for organisation purposes

Keywords: Big data analytics, Evaluation, Criteria

Big data analytics tools have many functions that are common or similar to one another. This is a problem for many organisations that are either interested or have deployed some of the tools. The problem arises because there is no mechanism on how to determine appropriateness of the tools within context in an environment. Some of the implications of the problem are that it is difficult to assess appropriateness of the tools in an environment, which sometimes result to duplication; and the value of the tools. The aim of this project was to propose a solution through a model that can be used to evaluate big data analytics tools for organisations' benefits. The qualitative method, case study approach and semi-structured interview technique were applied in the study. From the analysis, the role of criteria, business and IT alignment, governance, and skill-sets were revealed as critical factors. Based on the factors, a model was developed, which can be used as a building block through which evaluation of big data analytics tools in an organisation is carried out.

Introduction

Big data is not about the size of data, but characterised by three dimensions: variety, volume and velocity [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. Big data is processed by computerised software or applications, which is referred to as big data analytics [START_REF] Cukier | Big data: A revolution that will transform how we live, work, and think[END_REF]. Some of the big data analytics (BDA) tools include Hadoop, MapReduce [START_REF] Katal | Big data: issues, challenges, tools and good practices[END_REF]. There is increasing interest in the use of BDA from both academia and industry [START_REF] Hofacker | Big data and consumer behavior: Imminent opportunities[END_REF][START_REF] Ghazal | BigBench: towards an industry standard benchmark for big data analytics[END_REF], which can be attributed to the premise that the concept brings positive change to an environment that deploys it [START_REF] Demchenko | Defining architecture components of the Big Data Ecosystem[END_REF].

Organisations adopt big data concept to get some insights into the data to predict the future. Many organisations employ big data with the intention of improving activities and events such as sales, advertising, and future predictions [START_REF] Mgudlwa | Integration of social media with healthcare big data for improved service delivery[END_REF]. In the process of optimising the benefits of BDA, some organisations divide it into two sections: management, which is the extraction, retrieval and storage of datasets for transformation and decision-making purposes; and as the technology tool for analysing big data [START_REF] De Mauro | What is big data? A consensual definition and a review of key research topics[END_REF].

Despite the benefits that BDA seem to present, there are challenges with the concept, which include violation of privacy, security, data quality, slowness in query processing, scalability problems, integration complexities, and scarcity of big data skills [START_REF] Veiga | Performance evaluation of big data frameworks for large-scale data analytics[END_REF][START_REF] Suthaharan | Big data classification: Problems and challenges in network intrusion prediction with machine learning[END_REF][START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. Some of the challenges persist because there has been slow or lack of research, theoretical constructs, and academic rigor from management and perspective, in the areas of big data and BDA [START_REF] Sivarajah | Critical analysis of Big Data challenges and analytical methods[END_REF]. At large, there has also been a lack of research studies that comprehensively addresses the key challenges of BDA, or which investigates opportunities for new theories or emerging practices. This includes evaluation of big data analytics tools [START_REF] George | Big data and management[END_REF].

The main challenge is that there are no evaluation models or methods that can be used to assess BDA tools, and research have not focused on this aspect. Because of the variation of the big data analysis tools, some organisations have a challenge in selecting the most appropriate application for analysis [START_REF] Barton | Making advanced analytics work for you[END_REF]. There are some models, but they only focus on the evaluation of [START_REF] Delone | The DeLone and McLean model of information systems success: a ten-year update[END_REF][START_REF] Andargoli | Health information systems evaluation frameworks: A systematic review[END_REF]. As at the time of this research, the only study that focuses on evaluation of BDA tools, Hadoop, Spark, and Flink is from performance perspective [START_REF] Agostinho | Towards a sustainable interoperability in networked enterprise information systems: trends of knowledge and model-driven technology[END_REF]. This means that the gap remains, which this study aimed to address. Thus, the aim of this study was to propose a solution through a model that can be used to evaluate BDA tools in selecting the most appropriate for organisations' purposes. Thus, the objectives of this study were: (1) To determine the factors (technical and business) that influence the selection of big data analytics in organisations; and (2) To establish criteria for appropriateness of big data analytics tools in an organisation.

Literature Review

This review of literature was split into three areas in order to gain more insight on the specific areas, which are big data analytics, value of big data in organisations, and evaluation technique.

Big data analytics

The BDA approaches include predictive, descriptive and prescriptive [START_REF] Iyamu | A multilevel approach to big data analysis using analytic tools and actor network theory[END_REF]. The approaches focus on analysing unstructured, semi-structured and structured datasets, which can be images, audio or text [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. The analysis of big data involves machine learning applications or software like Hadoop, MapReduce, Apache and many more [START_REF] Katal | Big data: issues, challenges, tools and good practices[END_REF].

The predictive analytics tools focuses on historical data to predict the future. The analysis makes use of machine learning algorithms and statistical models to assess historical datasets for future predictions [START_REF] Davenport | Big data in big companies[END_REF]. The descriptive analytics focuses on current state, present datasets [START_REF] Daniel | Big Data and analytics in higher education: Opportunities and challenges[END_REF]. It uses historical datasets to identify patterns that can be reported or compared to current trend. The prescriptive analytics as an assessment of current situation to make decision [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. It combines both the descriptive and predictive analytics in finding new ways to achieve positive outcomes for organisations.

The efficiency of BDA is one of the reasons that organisation adopt and use the concept as a solution to decision making [START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]. Competitiveness is one of organisation's purposes in the use of BDA for predicting the future outcomes [START_REF] Davenport | Big data in big companies[END_REF]. It is argued that the BDA are used to deliver predictive insights in operations, drive real-time in decision making, and redesigning of business processes, in achieving results for organisational goals and objectives [START_REF] Wolfert | Big data in smart farming-a review[END_REF]. Despite the consistent argument that the BDA has a lot to offer to organisations, many challenges have been identified in the use of the concept [START_REF] Katal | Big data: issues, challenges, tools and good practices[END_REF], which include data validation, data cleansing, scalability of algorithms, and parallel data processing [START_REF] Najafabadi | Deep learning applications and challenges in big data analytics[END_REF].

Value of big data analytics in organisations

In recent years, there has been increase in the use of BDA tools, which can be attributed to the values that are associated to the concept [START_REF] Mgudlwa | Integration of social media with healthcare big data for improved service delivery[END_REF]. Grover et al. suggest that the concept of BDA can be used to promote productiveness and decision-making, which brings forth to organisations [START_REF] Grover | Creating strategic business value from big data analytics: A research framework[END_REF]. Two socio-technical features, convenience and interconnectivity are identified as the factors that influence the value of BDA in organisations [START_REF] Günther | Debating big data: A literature review on realizing value from big data[END_REF]. Some organisations are fascinated with the capabilities of the BDA in that it enable integration of data types, from different perspectives such size and variety [START_REF] Grover | Creating strategic business value from big data analytics: A research framework[END_REF].

In organisations, the value of BDA is viewed from different perspectives. One viewpoint, the value of BDA for an organisation is the process of collecting, storing and gaining insights to enhance organisation's performance [START_REF] Erevelles | Big Data consumer analytics and the transformation of marketing[END_REF]. From another angle, the ability to integrate, accessibility, response time are the value of the BDA in some organisations [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]. However, the BDA tools do not operate themselves, which is another challenge that is associated to the concept, and influence the type of value that is obtained. The emerging nature of the BDA makes skillsets scarce [START_REF] Wang | Big data analytics: Understanding its capabilities and potential benefits for healthcare organisations[END_REF][START_REF] Alharthi | Addressing barriers to big data[END_REF].

Evaluation models and techniques

In the field of IS, evaluation of software is critical, due to increasing reliance on it as solution in organisations. Another rationale for evaluating software is to ensure awareness and detect deficiency [START_REF] Pearlson | Managing and using information systems, binder ready version: a strategic approach[END_REF]. Thus, many computer systems undergo evaluation process [START_REF] Andargoli | Health information systems evaluation frameworks: A systematic review[END_REF]. Within the computing environment, there are various models and techniques for conducting evaluation of software and approaches. One of the techniques is that the evaluation begins with measuring technology functionalities, to align with the business requirements and needs [START_REF] Laudon | Management information system[END_REF].

There are numerous types of evaluation models and techniques. One of the evaluation technique is a Holistic Framework on IS evaluation which consists of five evaluation factors: Strategic value, Profitability, Risk, Successful Development and Procurement, and Successful Use and Operations [START_REF] Hallikainen | A holistic framework on information systems evaluation with a case analysis[END_REF]. Another rationale for evaluation in IS field is to mitigate against risk in projects and assess effectiveness of systems in an organisation. Also, there is no limit to evaluation is that there will always be new factors to evaluate [START_REF] Irani | Developing a frame of reference for ex-ante IT/IS investment evaluation[END_REF].

Research methodology

Based on the objective of the study, which was to examine and understand the factors that influence big data analytics and its appropriateness, the qualitative method, case study approach were employed in the study. This means that individuals and groups opinions and views was sought, which the qualitative method allows. The qualitative research is mostly employed in a scientific research to collect data, find answers to questions and draw conclusion from the findings produced [START_REF] Iyamu | Collecting qualitative data for information systems studies: The reality in practice[END_REF]. The focus of the qualitative method is not about numbers or statistics, it rather on human reactions such as feelings, emotions, and experiences in their real world [START_REF] Kumar | Research methodology: A step-by-step guide for beginners[END_REF]. The qualitative approach is mostly associated with subjectivism [START_REF] Walliman | Research methods: The basics[END_REF].

The case study approach was employed mainly because it focuses on the in-depth of the phenomenon being studied. Yin defines case study as an adaptable and openminded technique of information gathering [START_REF] Yin | Case study research and applications: Design and methods[END_REF]. The approach is broad, meaning a case can be an individual, an organisation, a community, a group, or an event [START_REF] Neuman | Social research methods: Qualitative and qualitative approaches Boston[END_REF]. The Plan Investments organisation was used as a case in the study. The organisation is a financial institution situated in Cape Town. It has over 150 employees at the time of this study.

Based on the level of the study, a criterion was used for a selection of participants. The criterion included the following: (1) organisation that employ big data analytics tools, and grants permission for the study; (2) participants should have be with the organisation for at least a year. Within the case study, data was collected. Participants that were interviewed included IT manager and Architects.

The semi-structured interview technique was used to collect data. This was to ensure flexibility during the process, in asking questions, which ultimately increases the chances of collecting rich data. The interviews was conducted with interviewees on one-on-one basis. The venue of the interviews was decided by the interviewees, to ensure their comfort. The interviews was tape recorded on the permission of the interviewees. In addition, notes were taken during the interviews, to increase comprehensiveness of the data collected.

The hermeneutics approach was applied in the analysis of the data, from the perspective of the interpretivist approach [START_REF] Butler | Towards a hermeneutic method for interpretive research in information systems[END_REF]. The hermeneutics approach proposes a method for understanding textual data [START_REF] Boell | Literature reviews and the hermeneutic circle[END_REF]. The approach helps to clarify and guide an understanding of objects and subjects in an interpretive manner [START_REF] Myers | An introduction to qualitative research in information systems[END_REF]. Interpretivist approach is about understanding phenomenon from individuals' viewpoints by exploring interaction among people [START_REF] Scotland | Exploring the Philosophical Underpinnings of Research: Relating Ontology and Epistemology to the Methodology and Methods of the Scientific, Interpretive, and Critical Research Paradigms[END_REF]. The hermeneutics approach was used from two main angles in the analysis: [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF] the relationships that exist between humans and big data in an organisation; and (2) the interactions that happen between humans in one hand, and on another between humans and big data. Through understanding of the relationships and interactions that happen between: human-to-human; and human-tobig data, the objectives of the study was achieved.

Case study: Data Analysis

Based on the objectives of the study as stated in section above, data was collected as discussed in the section above. The participants in the study were coded, for the following reasons: (1) To adhere to research ethics of the university (CPUT); (2) To preserve the identity of the participants; and (3) To respect the policy of the organisation that was used a case in the study. Thus, the code is as follow: participant-codename, page#: line#. PuInter01, Pg2: 3-5 -this means: participant 1, page 2, line 3 to 5 of the interview transcript.

The data was analysed following the hermeneutics approach from the interpretivist perspective. The hermeneutics approach was used in the analysis from the interpretivist perspective. The analysis focuses on two main areas: [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF] The relationships that exist between humans and big data in an organisation; and [START_REF] Cukier | Big data: A revolution that will transform how we live, work, and think[END_REF] The interactions that happen between humans in one hand, and on another between humans and big data. This was to gain better understanding of the relationships and interactions that happen between: human-to-human; human-to-big data; and big data-to-big data, in achieving the objectives of the study. The analysis was conducted according to the objectives of the study, which were (1) to determine the factors that influence the selection of BDA in organisations; and (2) to establish criteria for appropriateness of BDA tools in an organisation.

The factors that influence the selection of big data analytics in organisations

Big data is not all about volume, but defined by its characteristics, which include the Vs, volume, velocity, veracity, variety, and value [START_REF] Gandomi | Beyond the hype: Big data concepts, methods, and analytics[END_REF]. The tools that are employed in the analyses of big data are referred to as analytics tools. Some BDA tools/software have the capabilities to process datasets which are employed by organisations for various purposes. There are different types of big data analytics tools, which include diagnostic, descriptive, predictive, and prescriptive [START_REF] Iyamu | A multilevel approach to big data analysis using analytic tools and actor network theory[END_REF]. Through the use of big data analytics tools organisations make effective and appropriate strategic decisions. Big data analytics is on one hand sophisticated. On another hand, the same sophistication makes it complex. Thus, many people including organisation are sometimes confused about the purposes as well as the types of big data analytics. According to the senior manager in the organisation:

We are using Pentaho data integrator and Pentaho business analytics for the analysis of our big data (PuInter01, Pg1:32-34).

The BDA topic appears to be an overwhelming subject in the organisation, as it focuses on the bigger picture in terms of the terminology that comes with the tools. In the organisation there was a process that was followed in the selection of BDA tools. The process was sequential, meaning one step after the other. The primary components of the process include an understanding and formulation of organisational needs, exploration of available analytics tools, and evaluation of the tools within the context of the organisation. In the evaluation, the components were viewed from the aspects of technical and non-technical factors.

An organisation's goals and mission are a guide to choosing tools that suit its business needs. The selection of analytics tools does not only depend on the business goal, humans' involvement was critical, particularly in the decision-making as well as consideration of the users. Individuals such as business mangers seem to influences the decision in the selection of BDA tools. Nonetheless in the organisation it appears to be person leading the IT unit that agrees and takes ownership of the selecting the tools employed, however the tool that is selected has to be aligned with the business objectives. According to one of the participants, a senior architect in the organisation:

The organisation's requirements determine the selection of the tool. The challenge is mostly in formulating requirements, which include processes and events (Pulnter02, Pg6:20-23).

In the organisation the input of other employees that are using these tools influences the selection of the tools being employed in the organisation. Despite the requirements from the business the staff consider and evaluate the technical and nontechnical factors of the tools because they are equipped with knowledge of how the tools work. According to an IT senior manager in the organisation: This is not a visualization tool it is more of a data analytics tool. It sometimes get restricted by the ability of the user. Whereas some other tools basically aid the users towards achieving their objectives (Pulnter01, Pg3:1-3).

Despite the existence of numerous number of big data analytics tools, the organisation did choose few that were suitable for its business processes, activities and events. In the organisation the evaluation phase covers a lot of aspects. The aim of the organisation is to have a tool that can put the organisation on the direction of success. It is also to get big data analytics tools that are flexible and scalable. They seem to look at the knowledge they have on the tool itself. There seem to be a lot that the organisation covers in terms of creating an environment that the employees are familiar with.

Criteria for appropriateness of big data analytics tools in an organisation

Criteria is a set of standards and principles purposely for guide decision-making, and measuring scope and boundaries of actions in an activity or environment, the standards and principles are embedded with factors [START_REF] Etzioni | Mixed-scanning: A" third" approach to decision-making[END_REF]. In the organisation, criteria was formulated, and used in the selection of BDA tools. The criteria consisted of factors, such as cost, supportability, features, and skills required to use and support the tools. Despite a set of criteria, familiarity with a tool was most important. According to one of the participants:

Cost, knowledge of skills are some of the factors that are required in the evaluation of the tools. We have got to understand our future direction in line with the tool (PuInter01, Pg2:5-7).

In the organisation the criteria was used as guidelines in selecting a tool that was intended to benefit the organisation from return on investment point of view. This means that the also act as principle, which guided the employees in their focus and actions, and did not allow easy move from one tool to another. According to a senior architect in the organisation: Some of the tools are very well marketed by the owners and vendors. As a result, without requirements, there is a high possibility of selecting inappropriate tools (PuInter02, Pg3:14-16).

There were requirements two, business and IT units that formed the criteria, which was enabled by communication. Through the communication between the units including management team, the criteria was defined. The management of both IT and business units of the organisation were involved in gathering of the requirement that were used to formulate the criteria. This was primarily because they knew better knowledge about the current and future states of the organisation. Although there was no particular explanations why certain big data analytics tool was selected from the employees that were involved. This is attributed to the fact that the management of the organisation takes ownership of the process and the tool that was selected.

Despite the strengths of the tools that were employed in the organisation that has been used as a case in the study, there are challenges that have been highlighted. The main challenges that the organisation seem to be facing with the tools that were currently employed is about scarcity of skilled personnel, to use and provide technical support. In achieving the organisation' objectives, the current tools requires special skills, a person with an understanding of the functionalities and operations of the tools. One of the participants explains:

Our current tool is focuses more on data specialisation. It is therefore unrestricted to data analysis and management (PuInter01, Pg2:35-37).

Looking for a tool that would cater for everything is almost impossible. The organisation highlighted a challenge while selecting the appropriate tool from the limitless tools/applications that are available in the market. Without a properly defined requirements and formulation of criteria, there will always be challenges in selecting BDA tools. Also, organisation would change the tools frequently as they get influenced by excitable new functions that the some of the tools present.

Changing the tools from one BDA tools to another takes a lot effort and can be costly. The organisation seem to have a strategy, which can accommodate such situation. In the organisation the change is accepted only when there is a tool that offers better features than the one that is currently in use. Also, the organisational change leans towards goals and objectives.

Findings and Discussion

Based on the on the analysis as presented above, four factors were found to be critical in shaping and influencing the selection of BDA tools in an organisation. The factors were revealed based on the understanding of the analysis, which come from subjective reasoning. As shown in Figure 1, the factors are criteria, business/IT alignment, governance, and skill-sets. The factors are based on organisational vision. The factors are therefore a set of building block, as discussed below.

The role of criteria

Criteria is a set of guidelines that are built on principles and standards that can be used to evaluate the BDA tools for organisations [START_REF] Cai | The challenges of data quality and data quality assessment in the big data era[END_REF]. In this study, criteria consist of two main components, which are the business requirements and IT requirements. Business requirements is mostly connected with the goals, mission and objective, which come from the organisation vision. Business requirements work toward paving and setting what the business seeks to achieve. The IT requirements are based on attributes such as technology types, co-existence and compatibility, capacity, and flexibility of technologies. The IT requirements seek to enable and support the business requirements toward achieving the objectives of the organisation. This also means IT can be a constraint in that deficiency within the IT will have negative impact on the business requirements.

Based on both business and IT requirements, the set of criteria can be used as a control mechanism for selecting the most appropriate big data analytics tools for organisational purposes. The vital role of the criteria is that the organisation have rich understanding in what they want to do and what will fit the organisation needs. This also means that there is an evaluation process undertaken in the selection of the tools. Another significance factor in creating criteria is that it minimizes the chances of duplicating analytics tools for the same or similar purpose, and it mitigates against risks.

Business and IT alignment

The concept of Business and IT alignment is not new, it has been explored or applied in various ways. Also, it has been expressed in several terms such as, linkage, integration, and agreement. However, alignment does not refer to being aligned or not aligned, rather it how the relationship needs to be adjusted based on business unforeseen event [START_REF] Luftman | Enhancing the measurement of information technology (IT) business alignment and its influence on company performance[END_REF]. Some of the most important and crucial contributors identified in the alignment between business/ IT in the selection of BDA tools are communication and uniformity. Communication plays a significance role between the business and IT units in decision making. In addition, the uniformity of how things are done, following processes and policies of an organisation is important. Lack of alignments between the business and IT units can cause chaos in selecting BDA tools. As a result, working environment can become unstable, and leads to deploying applications that cannot serve the organisational purpose. It is clear that lack of alignment between business and IT units have huge implications, such as cost prohibitive, and technology compatibility risks for an organisation in selecting BDA tools.

Governance

Governance consists of standards, principles, and policies [START_REF] Shaanika | Developing the enterprise architecture for the Namibian government[END_REF]. It gives a structural guide to specialists and managers in decision-making. It is also procedural, which gives power to relevant authority to make decisions [START_REF] Bennett | Rules, principles and judgments in accounting standards[END_REF]. Thus, governance can be used to guide the selection of appropriate BDA tools in an organisation. There is means that the selected BDA tools adhere to standards and principles of the organisation. Selection of the analytics tools is one thing, and its use is another thing. The use of the BDA tools is as critical as the selection, which therefore also require guidance. Governance can be used to guide the use and management of the tools, to avoid technology conflicts and abnormalities. Lack of governance principles in organisation promote poor product quality. Also, it allows employees to avoid or shift accountability and responsibility as they deemed.

Skill-set

Big data analytics is an emerging concept, particularly in developing countries such as South Africa. As a result, there is scarcity of skill-set in the area of BDA, which affects the slow deployment of the concept in South African organisations. The skill-set play a very critical role in the deployment and use of BDA [START_REF] Ahmed | The role of big data analytics in Internet of Things[END_REF]. The noticeable reasons of the complexity of BDA require data scientist to perform. The machine learning applications designed to analyse big data requires skills of data scientist.

Although it is crucial to have the skill-set, what is even more important is retention of the personnel. Retention allows an organisation to keep their trained and skilled personnel as long as possible. This saves cost of upskilling, which can be very prohibitive, and affect return on investment. Lack of skills lead to incorrectness of data and operations falling apart, which influence decision-making, and ultimately affect business goal negatively.

Conclusion

This study that was conducted is proposed to assist organisation that wish to deploy and those that have already deployed BDA tools by doing an evaluation of the tools to seek for the tools that suit the organisations goal. In this study organisations will get a better understanding of the process of selecting an appropriate tools using criteria as guide. This study can be of usefulness in organisation that seek to understand the value of having a criteria to evaluate the BDA tool in the market. The research was limited to evaluation of BDA tools. This means that it excludes other activities such as deployment and readiness, as well as the application that are connected to BDA tools.

Fig. 1 .

 1 Fig. 1. Building Blocks