Conference Paper

The Role of Data Analytics in Startup Companies: Exploring Challenges and Barriers

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Abstract

The advancement in technology is transforming societies into digital arenas and paves the way towards the achievement of digital transformation. With every transaction in the digital world leading to the generation of data, big data and their analytics have received major attention in various fields and different contexts, examining how they may benefit the different actors in the society. The present study aims to identify how startups that develop products with both software and hardware parts can generate value from data analytics and what challenges they face towards this direction. To this end, we performed a multiple-case study with early-stage startups and employed qualitative analysis on a dataset from 13 startups. Through semi-structured interviews, we examine how these companies use data analytics. The findings show that although the benefits from data analytics are clear, multiple barriers and challenges exist for the startups to be able to create value from them. The major ones are about their resources, including human skills, economical resources, as well as time management and privacy issues.

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... However, there are also challenges and obstacles in using big data in startups. Some of these challenges include managing large volumes of data, ensuring data quality and security, and creating suitable infrastructures for processing and analyzing this data (Berg et al., 2018). Despite these challenges, big data offers significant opportunities. ...
... The findings of this research indicated that effective use of big data can help startups identify new market opportunities, improve production processes, and develop innovative products (Behl, 2020). These results are aligned with previous research emphasizing the vital role of big data in improving company performance and increasing their competitiveness (Behl, 2020;Berg et al., 2018). However, there are also challenges in using big data, including managing large data volumes, ensuring data accuracy and security (Berg et al., 2018). ...
... These results are aligned with previous research emphasizing the vital role of big data in improving company performance and increasing their competitiveness (Behl, 2020;Berg et al., 2018). However, there are also challenges in using big data, including managing large data volumes, ensuring data accuracy and security (Berg et al., 2018). These challenges require careful attention and planning to overcome. ...
Article
This study aims to explore the impact of big data on product development strategies within startups, focusing on how it influences innovation, operational performance, and market competitiveness. This qualitative research employed semi-structured interviews with managers and experts from various startups actively utilizing big data in their operations. The study design facilitated an in-depth understanding of the practical applications, challenges, and opportunities presented by big data in the context of startups. Three main themes were identified: "Product Development Strategies," "Challenges of Using Big Data," and "Opportunities Arising from Big Data." Each theme comprised several categories, including market analysis, product innovation, customer engagement, data management challenges, data accuracy and security, new customer insights, product optimization, and new product development. The study concludes that big data plays a crucial role in enhancing the innovativeness and competitiveness of startups by providing valuable insights for product development and market strategies. However, startups face challenges related to data management, accuracy, and security that must be addressed to fully leverage big data's potential.
... Start-up companies can respond to the rapidly changing needs and demands of consumers. They prefer ad-hoc development approaches, limiting administrative costs that can compromise business experimentation, depending on team members and resources (Berg et al., 2018). They have the ability to conquer new markets, using information and communication technologies and the approaches they use (Berg et al., 2018;Khan et al., 2023). ...
... They prefer ad-hoc development approaches, limiting administrative costs that can compromise business experimentation, depending on team members and resources (Berg et al., 2018). They have the ability to conquer new markets, using information and communication technologies and the approaches they use (Berg et al., 2018;Khan et al., 2023). ...
... In addition to financial, human resources, support mechanisms, environmental challenges (Salamzadeh & Kawamorita Kesim, 2015), startup companies have challenges related to product development and application of innovative methods (Berg et al., 2018). Likewise, political circumstances such as changes in legislation can accelerate or inhibit some business processes. ...
Article
Start-up companies are generators of economic development. Given that these companies are characterized by a high degree of risk at the beginning of their operations, it is very important to properly formulate development and positioning strategies. This paper uses strategic tools to analyze the internal and external factors affecting the operations of a start-up company. The analysis of internal and external factors was carried out on the case of a start-up company established with the aim of connecting consumers and producers of organic agricultural products through a mobile application. The significance of this paper lies in providing guidelines to the company's management for defining business development and market positioning strategies. The results of this research serve as an incentive for further studies related to start-up companies, both at the local and regional levels.
... In today's digital era, people generate a lot of data all the time from various sources, through various digital media platforms and digital services which then lead to a big data ecosystem and business analytics (Pappas et al., 2017). Startups and entrepreneurs use data to increase value, gain competitive advantage, and improve various aspects of society (Berg et al., 2018;Otero and Peter, 2014). Startup is a new venture that produces cutting-edge technology and has a huge impact on the global economy (Giardino et al., 2016). ...
... In the context of extreme uncertainty and limited economic, human and physical resources, startups have unique challenges related to product development and their innovation methods (Giardino et al., 2015). Startups operate in high-risk, fast-changing and competitive environments, which is why continuous experimentation is essential to quickly learn about and bring products to market (Berg et al., 2018). There is an increasing literature on how big data analytics can generate business or social value . ...
... Startups must respond to rapidly changing customer needs and demands (Bosch, 2015) both by speeding up the decision-making process and the design process (Pantiuchina et al., 2017). The design process acceleration is carried out using a prototype iteration approach to validate product-market fit quickly (Berg et al., 2018). ...
... In today's digital era, people generate a lot of data all the time from various sources, through various digital media platforms and digital services which then lead to a big data ecosystem and business analytics (Pappas et al., 2017). Startups and entrepreneurs use data to increase value, gain competitive advantage, and improve various aspects of society (Berg et al., 2018;Otero and Peter, 2014). Startup is a new venture that produces cutting-edge technology and has a huge impact on the global economy (Giardino et al., 2016). ...
... In the context of extreme uncertainty and limited economic, human and physical resources, startups have unique challenges related to product development and their innovation methods (Giardino et al., 2015). Startups operate in high-risk, fast-changing and competitive environments, which is why continuous experimentation is essential to quickly learn about and bring products to market (Berg et al., 2018). There is an increasing literature on how big data analytics can generate business or social value . ...
... Startups must respond to rapidly changing customer needs and demands (Bosch, 2015) both by speeding up the decision-making process and the design process (Pantiuchina et al., 2017). The design process acceleration is carried out using a prototype iteration approach to validate product-market fit quickly (Berg et al., 2018). ...
... There is still uncertainty, however, how software startups understand and apply analytics throughout the product as well as the business development. The role of analytics in startups is unknown despite the widespread use of analytics in other businesses [2,17]. Therefore, the current study seeks to address this gap. ...
... In another related study, while investigating the role of data analytics in startup companies, Berg et al. [2] presented challenges and barriers faced by startups. The study claims that startups are aware of the benefits of applying analytics, however, they are also facing challenges in implementing it. ...
... A similar situation is reported by another startup at Mixpanel platform (2). The startup experienced a sudden drop in the conversion rate and later it was found that a minor change on the home page has caused this drop. ...
... to market penetration, and seeking customer validation in the short-run [3]. ...
... Several studies have been conducted to unlock entrepreneurial business potential and identify key challenges such businesses observe [3], [5]. With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. ...
... With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. Yet, there is evidence that early-stage startups face the challenges of skills, capital, market uncertainty, technological uncertainty, time management, and privacy issues to generate values that contribute to poor startup success rates [3], [5], [12]. Hartmann et al. [6] provided an extensive discussion on the taxonomy of data-driven business models used by startups, encompassing seven key business activities: free data collection and aggregation, analytics-as-a- service, data generation and analysis, free data knowledge discovery, data-aggregation as-a-service, and multi-source data mash-up and analysis (see also [7]). ...
Article
Full-text available
Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.
... to market penetration, and seeking customer validation in the short-run [3]. ...
... Several studies have been conducted to unlock entrepreneurial business potential and identify key challenges such businesses observe [3], [5]. With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. ...
... With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. Yet, there is evidence that early-stage startups face the challenges of skills, capital, market uncertainty, technological uncertainty, time management, and privacy issues to generate values that contribute to poor startup success rates [3], [5], [12]. Hartmann et al. [6] provided an extensive discussion on the taxonomy of data-driven business models used by startups, encompassing seven key business activities: free data collection and aggregation, analytics-as-a- service, data generation and analysis, free data knowledge discovery, data-aggregation as-a-service, and multi-source data mash-up and analysis (see also [7]). ...
Conference Paper
Full-text available
Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.
... There is still uncertainty, however, how software startups understand and apply analytics throughout the product as well as the business development. The role of analytics in startups is unknown despite the widespread use of analytics in other businesses [2,17]. Therefore, the current study seeks to address this gap. ...
... In another related study, while investigating the role of data analytics in startup companies, Berg et al. [2] presented challenges and barriers faced by startups. The study claims that startups are aware of the benefits of applying analytics, however, they are also facing challenges in implementing it. ...
... A similar situation is reported by another startup at Mixpanel platform (2). The startup experienced a sudden drop in the conversion rate and later it was found that a minor change on the home page has caused this drop. ...
Preprint
Full-text available
Analytics plays a crucial role in the data-informed decision-making processes of modern businesses. Unlike established software companies, software startups are not seen utilizing the potential of analytics even though a startup process should be primarily data-driven. There has been little understanding in the literature about analytics for software startups. This study set out to address the knowledge gap by exploring how analytics is understood in the context of software startups. To this end, we collected the qualitative data of three analytics platforms that are mostly used by startups from multiple sources. We covered platform documentation as well as experience reports of the software startups using these platforms. The data was analyzed using content analysis techniques. Four high-level concepts were identified that encapsulate the real understanding of software startups on analytics, including instrumentation of analytics, experimentation, diagnostic analysis, and getting insights. The first concept describes how startups set up analytics and the latter three illustrate the usage scenarios of analytics. This study is the first step toward understanding analytics in the software startup context. The identified concepts can guide further investigation of analytics in this context. It also provides some insights for software startups to set up analytics for data-informed decisions. Given the limitation of the data used in the study, the immediate next step is to ground as well as validate the acquired understanding using the primary data, by directly interacting with software startups.
... Instagram is a well-known example that used BA to alter their business model, whereby during the early-stages, its founders used BA to analyse app data and spot users' preferences regarding posting photographs (Steer, 2021). However, researchwise, although there is evidence that smaller firms use BA (e.g., Behl et al., 2019;Berg et al., 2018;Sayyed-Alikhani et al., 2021), there is a paucity of research on how exactly SMEs use this technology for business model transformations. ...
... Start-ups tend to underutilise and exploit analytics as means to better understand their customer needs and offer relevant services (Behl et al., 2019), e.g., to design customer acquisition and retention strategies (Sayyed-Alikhani et al., 2021), for product development purposes (Berg et al., 2018), and more rarely to improve their internal process e.g., to prioritise projects (Zamani et al., 2021). Even in these cases in which BA might be underutilised, BA introduce an important opportunity for start-ups and SMEs more generally (Sheng et al., 2020;van Rijmenam et al., 2019). ...
... A third contribution lies with our analytical explanation regarding BA exploitation by SMEs and the relationship between Business Analytics and Dynamic Capabilities in the SME context. Existing studies show that BA is underutilised (Behl et al., 2019) by SMEs and mainly present successful exploitation of analytics by SMEs merely for customer relation purposes (e.g., Behl et al., 2019;Berg et al., 2018;Sayyed-Alikhani et al., 2021), but not for other purposes such as improvement of their internal processes and transformation of their business models. In this study we offer evidence on how Business Analytics may alert a start-up with regard to threats and opportunities; signpost them to potential opportunities and ways of responding; and help them assess the viability of envisaged BMs. ...
Article
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The COVID-19 pandemic has had an unprecedented impact on many industry sectors, forcing many companies and particularly Small Medium Enterprises (SMEs) to fundamentally change their business models under extreme time pressure. While there are claims that technologies such as analytics can help such rapid transitions, little empirical research exists that show if or how Business Analytics (BA) supports the adaptation or innovation of SME business models, let alone within the context of extreme time pressure and turbulence. This study addresses this gap through an exemplar case, where the SME actively used location-based business analytics for rapid business model adaptation and innovation during the Covid crisis. The paper contributes to existing theory by providing a set of propositions, an agenda for future research and a guide for SMEs to assess and implement their own use of analytics for business model transformation.
... to market penetration, and seeking customer validation in the short-run [3]. ...
... Several studies have been conducted to unlock entrepreneurial business potential and identify key challenges such businesses observe [3], [5]. With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. ...
... With this is the potential of digital footprints generating big data for predictive and visual analytics [6], [13]. Yet, there is evidence that early-stage startups face the challenges of skills, capital, market uncertainty, technological uncertainty, time management, and privacy issues to generate values that contribute to poor startup success rates [3], [5], [12]. Hartmann et al. [6] provided an extensive discussion on the taxonomy of data-driven business models used by startups, encompassing seven key business activities: free data collection and aggregation, analytics-as-a- service, data generation and analysis, free data knowledge discovery, data-aggregation as-a-service, and multi-source data mash-up and analysis (see also [7]). ...
Book
Full-text available
Data remained to be one of the most underutilized resources of the digital economic system. As the agrarian economy relies on land, the digital economy relies on data as one monetized asset class. In this context, a data-powered business model is not a new notion. However, the practice is more common to established businesses than early-stage startups that strive to penetrate a market. This chapter provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed a data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this chapter is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.
... In addition to financial, human resources, support mechanisms, environmental challenges (Salamzadeh & Kawamorita Kesim, 2015), startup companies have challenges related to product development and application of innovative methods (Berg et al., 2018). Likewise, political circumstances such as changes in legislation can accelerate or inhibit some business processes. ...
... An additional challenge for start-up companies and their further development can be the use of information technologies, as well as investment in them. Digitization and the use of new technologies can help these companies achieve sustainability (Berg et al., 2018;Khan et al., 2023;Foris, et al, 2022). ...
Article
Full-text available
Abstract: Start-up companies represent a generator of economic development. Bearing in mind that these companies are characterized by a high degree of risk at the beginning of business, it is very important to correctly formulate the strategy of development and positioning. The paper uses strategic tools to analyze internal and external factors that influence the business of a start-up company. The analysis of internal and external factors was done on the case of a start-up company founded with the aim of connecting consumers and producers of organic agricultural products through a mobile application. The importance of this work is reflected in the aspect of giving guidelines to the company's management in order to define the strategy of business development and positioning in the market. The results of this research represent an incentive for further research related to start-up companies both at the local and regional level. Keywords:start-up companies, macro-environmental factors, strategic analysis tools.
... However, the uncertain nature of startups and their focus on innovation within limited resources make them promising candidates to practice it. Prior research concludes that startups realize the benefits of analytics [11] and analytics practices should be present as a toolkit to support startup operations in cutting operational costs, supporting decisions, and getting insights [7]. On the contrary, the existing literature sheds no light on the analytics practices currently employed by these companies. ...
... Together these studies report challenges that software startups face and comprehend how software startups understand analytics. In contrast, Berge et al. [11] studied hardware startups and explored benefits and challenges while utilizing analytics. ...
Chapter
Full-text available
Analytics is becoming known for providing exceptional opportunities to companies. However, companies are not utilizing its full potential despite the widespread benefits. In particular, analytics practices are imperceptible when we talk about small yet innovative companies like software startups. Nevertheless, startups’ uncertain nature and focus on innovation make them promising candidates to increase the odds of success using analytics. In this paper, we investigate the key practices applied by software startups while conducting analytics. We address our research question through a multiple case study performed with three startup companies at distinct startup stages. Thematic data analysis led us to observe nine analytics practices applied by these startups. The identified analytics practices span a spectrum of activities characterized by the analytics framework. Our findings provide insights for software startup companies to introduce or enhance analytics in their specialized context.
... Big data can be used in different ways, for example, to forecast the market conditions, optimise the functionalities of a product, drive supply chains, and/or identify patterns through modelling (Auschitzky et al., 2014;Sanders, 2016). However, finding relevant information from big data comes with numerous challenges, and studies suggest that most of the company data remains unused (Berg et al., 2018;Gerschütz et al., 2023;Ronkainen and Abrahamsson, 2003). Traditional data, on the other hand, is a data that can be any set of information that provides value, for example sales data (Loukides, 2011). ...
... It is thus pertinent to use methods such as data mining in Early Validation of High-Tech Startups by Using (Big) Data the early stages to find relevant information that can be used by the startup (Gerschütz et al., 2023). There are other impact factors that influence (big) data adoption in the early stages, for example, startups often cannot employ capable people who are efficient in data analysis due to lack of financial resources or time to get the required results (Berg et al., 2018). Using analytics tools is often not part of the short-term strategic focus of the startups and it becomes difficult to integrate it into their plan. ...
Article
Full-text available
This study focuses on high-tech startups in the Norwegian context and investigates the use of data and/or big data to support validation at the early stage of development. Early-stage startups often fail due to lack of validation and incur loss to all the stakeholders involved in the process. Early validation through a lean or agile approach can help these young companies to manoeuvre through a turbulent external environment. The results from the study show that big data and/or data can act as a support at this stage but is not necessarily the sole solution to the problem. There are various barriers that need to be addressed for successful data-based validation. Based on multi-case study research method, this study proposes an early validation user guide (EVU) to overcome these barriers and make data adoption easier. The EVU can provide startups support to show how and when to use data and/or big data depending on the market context. The study thus contributes to the current body of knowledge of innovation and entrepreneurship concerning validation for early stage startups and can also guide practitioners in validating their startup ideas.
... However, startups face significant challenges, with many failing within their first year due to factors such as lack of new ideas, unfavorable working environments, and insufficient financial support (Musyck & Robota, 2018). To overcome these challenges, startups often rely on data analytics and agile development practices to increase their chances of success (Berg, Birkeland, Pappas, & Jaccheri, 2018) (Bosch, Olsson, Björk, & Ljungblad, 2013). Moreover, the entrepreneurial ecosystem plays a vital role in supporting startups, providing them with necessary resources, encouragement, and guidance during their early stages (Chaudhari & Sinha, 2021) (Condom-Vilà, 2020. ...
Article
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In 2023, startups in Algeria witnessed significant development, making them a promising sector for diversifying the Algerian economy and reducing dependence on hydrocarbons. This progress is due to various measures taken by the government, including the issuance of laws and regulations to support and accompany startups, the creation of electronic platforms for registering, protecting, and providing technical support to these enterprises, and the establishment of specific funding sources for startups. Within this framework, the Algerian Ministry of Higher Education and Scientific Research (MESRS) has outlined a special program to contribute to this new direction by involving universities in supporting the creation of startups through Ministerial Decision No. 1275. This study aims to analyze and evaluate this new initiative adopted by the Ministry of Higher Education by examining several startup projects initiated by students as part of their graduation thesis discussions at the Faculty of Economic, Commercial, and Management Sciences (FSECSG) at Annaba University and the Higher School of Management Sciences (ESSG) in Annaba. The study concludes that this new vision adopted by the Ministry of Higher Education has several positive aspects that should be valued, including: supporting the linkage of Algerian universities with their socio-economic environment, supporting the local economy, and enhancing the level of Algerian universities. However, this new approach also has several weaknesses and negative points that need solutions, such as the lack of consideration in many startup projects for key aspects like SWOT analysis, legal aspects, and innovation. Additionally, the experience is generally new, resulting in a lack of an encouraging ecosystem for startups in Algeria. JEL Classification: M13, M130, I23
... deliver ongoing value to customers [5]. Unlike the traditional upfront-sales models, circular models adopt a continuous service-oriented process that redefines the concepts of value and ownership. ...
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Currently, there has been a tendency for companies to focus on the development of their businesses following circular economy trends associated with sustainability. European Community guidelines have established deadlines for the implementation of management strategies that allow creating and maintaining an effective and resource-efficient economic system, reducing the amount of energy and materials used in production. This study aims to analyze the existing literature review on the integration of sustainability and circular economy (CE) principles into business development. The research methodology is a systematic literature review using the SCOPUS database, in which 97 academic articles were analyzed after exclusion using keywords. The findings show that, after the analysis carried out in VOSviewer, business development, and circular economy are themes studied by many authors, with links related to the theme of sustainability. The conclusions indicate that CE and sustainability are interlinked, and companies must implement appropriate sustainability and CE strategies to increase their competitiveness and improve resource efficiency. These strategies can integrate innovative technologies, the use of sharing platforms, extending the useful life of products, recovering resources to minimize waste, and integrating sustainability concepts into business.
... A few recent studies [3][4][5] develop our earlier understanding of analytics for startups in terms of role of analytics in startup companies, analytics challenges for startups, and perception of startups regarding analytics. Much of the related work, in the field of software engineering, is focused on analytics about software and its associated artifacts [12]. ...
Conference Paper
Full-text available
Software startup companies operate under extreme conditions of uncertainty and with limited resources. These innovative companies face constant pressure to find a product-market fit, drive growth, and maintain competitive advantage. The nature of these companies makes them suitable candidates to practice analytics. Analytics can help software startups to use data in several ways e.g. make data-informed decisions, grow business, and provide value to users. However, startup founders tend to put off practicing analytics for a later time. In addition, the existing literature on startups does not provide paved paths to establish analytics in the context of startups. Therefore, to this end, we perform a gray literature review, to understand what startup practitioners say about analytics benefits and how can startups define analytics within their particular context. We utilized YouTube as a source of our data. After applying inclusion and exclusion criteria to 400 videos, we ended up analyzing 16 potentially relevant videos. We used thematic synthesis as well as quasi-statistics to analyze the data. Our results identify and report ten analytics benefits, and two key analytics practices to set up analytics in these competitive environments.
... The sources of big data are dynamic, have widely differing qualities, and are continuously updating (Dong and Srivastava, 2013). However, finding relevant information from big data comes with its challenges and barriers (Berg et al., 2018;Ronkainen and Abrahamsson, 2003). Startups often cannot employ capable people who are efficient in data analysis, nor do they have the financial or time resources to bring the required results. ...
Conference Paper
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Majority of the start-ups fail in the early stage of the development due to lack of validation. This paper focuses on high-tech start-ups and investigates the use of data and/or big data at this stage. The study found that early-stage start-ups fail because they create products or services not needed in the market. Early validation through an agile approach can help these young companies to maneuver through a turbulent external environment. The results show that big data or data can act as a support at this stage. However, there are various barriers that need to be addressed for successful data adoption. The paper proposes an early validation user guide (EVU) to overcome these barriers and make data adoption easier. The EVU can provide start-ups the tools to use big data or data as a support for early validation based on the market context of the start-ups.
... Third, having access to big data and being able to analyze them could become increasingly important to entrepreneurs and their ventures (Berg et al. 2018;Kleine-Stegemann 2021). The development of big data analytics capabilities, considered as a ''company's abilities to leverage on technology and talent to exploit big data'' (Ciampi et al. 2021, p. 2) -could therefore be critical for entrepreneurial actors to compete in highly dynamic and digitalized markets. ...
... It has a high potential opportunity to be developed and accepted by the market along with changes in more advanced technology today. However, these startups also work with high uncertainty, especially regarding customers and market conditions, and have a high failure rate [5], [6]. It is no wonder that most startups fail, as reported by Startup Genome (2019) [7] that only 1 in 12 startups is a success and the failure rate is more than 90 percent. ...
Article
Full-text available
Startups have great potential to grow and scale up their business quickly; moreover, they have an essential role in the growth of the country and the global economy. However, with the high risk of failure, startup success needs to be supported and concerned. The success of startups depends on market needs and expectations, which are currently highly uncertain, dynamic, and chaotic. Thus, it is necessary to identify and monitor customer preferences for startup products/services. This study identifies the customer preferences of two successful competitive food delivery startups, Go Food and Grab Food. With increasing customer opinions on social media, Twitter data can be used to explore customer needs and preferences. However, social media data like Twitter tend to be unstructured, informal, and noisy, so data mining mechanisms are needed. This study explores and compares customer preferences for successful startup products, using sentiment analysis and text mining methods, which have yet to be done in previous studies. The sentiment analysis results show the dominance of positive customer opinions and expressions of the products/services offered. Furthermore, customer product aspects reviewed positively and negatively by customers were analyzed more deeply using text mining to find the strength and weaknesses of these two businesses. The method and analysis of this paper help monitor customer opinions in real-time, both related to their satisfaction and complaints. Finally, the research results have been validated by comparing sentiment analysis classifications using machine learning and manual analysis by experts, which show an accuracy of 85% and 86% in Go Food and Grab Food reviews.
... Third, having access to big data and being able to analyze them could become increasingly important to entrepreneurs and their ventures (Berg et al. 2018;Kleine-Stegemann 2021). The development of big data analytics capabilities, considered as a ''company's abilities to leverage on technology and talent to exploit big data'' (Ciampi et al. 2021, p. 2) -could therefore be critical for entrepreneurial actors to compete in highly dynamic and digitalized markets. ...
Article
Full-text available
While recent research continues to emphasize the importance of digital entrepreneurship, the historical terminology of this field is often overlooked. Digital entrepreneurship tends to be considered a new phenomenon despite emerging in the early 1990s. Building on a scoping literature review, this study analyzes 1354 publications that use nine different terms interchangeably to describe the phenomenon of digital entrepreneurship. Based on the number of publications per year, three eras in the historical development of digital entrepreneurship research are outlined. Digital technologies are identified as external enablers, and certain practical events are considered to be influencing factors. The results show that recent research has not adequately recognized the contributions of previous publications and that the understanding of digital entrepreneurship is quite similar with regard to the terms used and over time. This study shows how emerging digital technologies, such as artificial intelligence, blockchain technology, and big data analytics, might shape the future of digital entrepreneurship research. The study occupies the intersection between entrepreneurship and information systems literature and its main contribution is to provide new insights into the eras of digital entrepreneurship from the past to the present and into the future.
... Very few studies have identified the importance of data analytics in startups and the idea that a new approach and methodology to manage employees could be an asset for small and medium enterprises (SMEs) is still blurred (Verma et al., 2020). Due to start-ups' unique characteristics of being less formal, dynamic, nimble, creative and relationship-driven, there is a need to identify key challenges or problem areas, which are more important than others for enabling DDDM in a start-up (Berg et al., 2018). ...
Article
Purpose Across industries, firms want to adopt data-driven decision-making (DDDM) in various organizational functions. Although DDDM is not a new paradigm, little is known about how to effectively implement DDDM and which problem areas to focus on in these functions. This study aims to enable start-ups to use DDDM in human resources (HR) by studying five HR domains using a narrative inquiry technique and aims to guide managers and HR practitioners in start-ups to enable data-driven decisions in HR. Design/methodology/approach This study adopts the narrative inquiry technique by conducting semi-structured interviews with HR practitioners and senior members handling HR functions in start-ups. Interview memos are thematically analyzed to identify repeated ideas, concepts or elements that become apparent. Findings The study findings indicate that start-ups need to have canned operational reports with right attributes in each of these HR domains, which members should use when performing HR tasks. Few metrics, like cost-to-hire in recruitment, distinctly surfaced relatively higher in importance that each start-up, should compute and use in decision-making. Practical implications Managers, HR practitioners and information technology implementation teams will be able to consume the findings to effectively design or evaluate HR processes or systems that empower decision-making in a start-up. Originality/value Start-ups have a fast-paced culture where creativity, relationships and nimbleness are valued. Prevalent decision models of larger organizations are not suitable in start-ups’ environments. This study, being cognizant of these nuances, takes a fresh approach to guide start-ups adopt DDDM in HR and identify key problem areas where decision-making should be enabled through data.
... Under the academic perspective, some of them are the explosion of data and social media, the proliferation of channels, and the shift in consumer demographics, etc. (Leeflang et al., 2014). Another academic research discovers that the issues are coming from the lack of resources, including worker skills, economic resources, as well as time management and privacy issues (Berg et al., 2018). However, under the industrial perspective, main obstacles faced by companies in the digital age come from the business's understanding the need to change, lack of a clear vision for a digital customer journey, ineffective gathering and leveraging of customer data, and inflexible technology stack and development processes (Tiersky, 2018). ...
Chapter
Rural Vietnam can be defined in three settings that include agriculture, rural development, and rural livelihoods, in which the agriculture has played a critical role in Vietnam's economy, contributing 20% to the GDP and 40% to the working population. This chapter attempts to identify the facilitators and potential barriers to rural entrepreneurship in Vietnam by following a qualitative research design examining the literature. Further, the chapter particularly attempts to identify the facilitators and potential barriers to develop rural entrepreneurship in the high-tech context. The analysis of the study illustrates the two most critical facilitators for rural entrepreneurship in Vietnam as “enterprise capitals” and “innovation including technology.” Further, the study also highlighted barriers such as “limited access to distribution channels” and “limited access to business support and technology.”
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In the digital realm, each transaction gives rise to a diverse range of data. The practice of data analysis has garnered significant attention across various domains and contexts, exploring its potential benefits for different stakeholders in society. The primary objective of this research was to examine the impact of data analytics capabilities on startups’ performance. To attain this objective, a cross-sectional study was undertaken, involving the surveying of 267 managers engaged in startups operating within the e-commerce sector. Employing structural equation modeling (SEM), both the measurement model and the structural model of the research were meticulously validated. The findings of this study unequivocally demonstrated that data analytics capabilities played a pivotal and affirmative role in augmenting the performance of these startups. Consequently, a series of recommendations were delineated, with the most noteworthy suggestion being the strategic transformation facilitated through substantial investments in robust data infrastructure and advanced tools.
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تهدف الدراسة إلى إبراز دور البيانات الضخمة كأداة استراتيجية لتمكين نمو وتطور المؤسسات الناشئة الرقمية، مما يجعلها قادرة على المنافسة بفعالية في سوق الأعمال الرقمية المتطور، كما تسلط الضوء على كيفية لعب البيانات الضخمة دورًا حاسمًا في نمو وازدهار المؤسسات الناشئة في العصر الرقمي. حيث تعتبر البيانات الضخمة (Big Data) موردًا استراتيجيًا للشركات الناشئة، إذ تمكنها من استخراج القيمة من مجموعات البيانات الضخمة لتحسين أدائها واتخاذ القرارات الأكثر دقة وفاعلية، وخلصت الدراسة الى أن البيانات الضخمة توفر رؤى عميقة حول سلوك المستهلكين واتجاهات السوق، كما تمكن المؤسسة الناشئة من فهم تفاعل المستخدمين مع منتجاتها وخدماتها، وبالتالي تحسين تجربة المستخدم وتلبية احتياجاتهم بشكل أفضل، كما تساعدها في تحسين عملياتها الداخلية، وتحديد الاحتياجات والمتطلبات الجديدة في السوق، وبالتالي تمكينها من الابتكار وتطوير منتجات وخدمات جديدة تلبي تلك الاحتياجات، إلا أن هناك صعوبات تتمثل بشكل خاص في الحماية والأمن، وأيضا كفاءة وجودة البيانات المستخدمة.
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Afetler ortaya çıktığı tarihten itibaren çok sayıda can ve mal kaybına, medeniyetlerin yok olmasına ve kitlesel göçlere neden olmuştur. Engellilik olgusu, engelli bireyin etkileşim içerisinde bulunduğu sosyal grupların farkındalığı oranında içselleştirilebilmektedir. Toplum içinde engelli bireyler yoksulluk, eğitim, rehabilitasyon, ulaşım, fiziksel çevre ve konut, erişilebilirlik/ulaşılabilirlik alanında sorun yaşamaktadır. Bu çalışmada afetlerin engelli bireyler üzerindeki etkisinin ele alınması amaçlanmaktadır. Afet öncesi yaşadığı toplumda herhangi bir zorlukla karşılaşmadan hayatını devam ettiren bir engelli birey afet nedeniyle var olan çevresel ve toplumsal düzenin bozulması sonucu hareket kısıtlılığı, erişim gibi sorunlarla karşılaşarak dezavantajlı duruma düşebilir. Afet öncesi sosyal hayatta damgalanan bireyler afet yönetim süreçlerinde göz ardı edilerek süreçlere dâhil olamayabilir. Afet öncesi planlama ve risk azaltma sürelerinde dikkate alınmayan ve paydaş olarak sürece katılmayan engelli bireyler afet durumlarında daha büyük sorunlar yaşamaktadır. Engelli bireyler afet öncesi sırası ve sonrasında birçok zorlukla karşılaşmaktadırlar. Engelli bireylerin karşılaşabileceği zorluklar; aile, arkadaş, bakım hizmeti veren birey gibi destek ağlarının bozulması, hareketliliğe yardımcı olan tekerlekli sandalye, protez, ortez gibi cihazların hasarı ya da kaybı, acil durum toplanma/barınma yerlerine ve erken uyarı veya genel uyarı mesajlarına erişimdeki zorluklar olarak sıralanabilir. Sonuç olarak afetlerde toplumun etkilenme biçimi, şiddeti ve etkiye verebildikleri yanıtlar farklı olmakla birlikte en fazla etkilenecek dezavantajlı gruplar engellilerdir. Engellilerin afetlerden etkilenme durumunda etkilenme düzeylerinde bireysel farklılıklar görülebilir. Engel durumuna göre oluşacak bu farklılar için engelli bireylere görüş sorulmalı ve ihtiyaç analizi yapılmalıdır. Özellikle afet eylem planı çalışmalarında engelli bireylerin hazırlıkların bir parçası olması sağlanmalıdır.
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Hardware startups are increasingly popular due to recent advancements in hardware technologies. Nowadays, hardware product development involves the process innovation not only at the hardware level but also at software components. The scarcity of knowledge on hardware startup product development motivates the authors to carry out an empirical investigation on five hardware startup companies. They found some common good practices among hardware startups (i.e., process definition, evolutionary development process, and document management). They reveal several factors that are different from software startups, such as low priority of product quality, product pipeline, and unrecognized product platform. They proposed an integrative process model of hardware product development that shows the connections between human factors in the startups, their speed-prioritized development processes, and the consequence of hindered productivity in the later phases. The model has some implications for hardware startup founders to plan for the trade-off between team, speed, quality, and later productivity.
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Organizations produce churns of complex, opaque, and meaningless data. Business intelligence systems (BIS) help organizations efficiently use their data in promptly making quality decisions. The limitations of conventional BIS in processing the volume, variety, and veracity of data available in organizations and the operational cost of maintaining its infrastructure gave rise to implementing Cloud BIS, which describes the deployment of BIS over the cloud platform enabling resource elasticity, moderate usage cost, unlimited resources, and offering greater value at a reduced cost than the traditional BIS. BIS aids a productive supply chain and given the complexities of Africa's supply chain and high volatility of its business environment, BIS over the cloud platform would be a great enabler. This chapter aims to provide an understanding of how BIS will transform supply chain management in Africa's emerging market.
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With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.
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It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping.
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Software startups operate under various uncertainties and the demand on their ability to deal with change is high. Agile methods are considered a suitable and viable development approach for them. However, the competing needs for speed and quality may render certain agile practices less suitable than others in the startup context. The adoption of agile practices can be further complicated in software startups that adopt the Lean Startup approach. To make the best of agile practices, it is necessary to first understand whether and how they are used in software startups. This study targets at a better understanding of the use of agile practices in software startups, with a particular focus on lean startups. Based on a large survey of 1526 software startups, we examined the use of five agile practices, including quality related (regular refactoring and test first), speed related (frequent release and agile planning) and communication practice (daily standup meeting). The findings show that speed related agile practices are used to a greater extent in comparison to quality practices. Daily standup meeting is least used. Software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.
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Social media websites have managed in a very short period of time to attract and maintain a massive user. Recognizing their potential, the vast majority of companies are deploying strategies in order to harness their potential in various ways, and ultimately, to establish their competitive position. Nonetheless, being relevantly novel, it still remains unclear as to how it is possible to make the most out of social media, especially in competitive and highly dynamic environments. As with any new technology, it is important to understand the mechanisms and processes through which social media can be of business value for companies in order to incorporate them into their competitive strategies. To this end, the present paper aims to provide a theoretical discussion leading up to a conceptual research framework that can help explain the mechanisms through which social media and analytics lead to competitive performance gains. The conceptual research framework builds on the resource-based view (RBV) and dynamic capabilities view (DCV) of the firm, and provides a synthesis of the two theoretical perspectives.
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Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multidisciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs.
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Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
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Software startups are newly created companies designed to grow fast. The uncertainty of new markets and development of cuttingedge technologies pose challenges different from those faced by more mature companies. In this study, we focus on exploring the key challenges that early-stage software startups have to cope with from idea conceptualization to the first time to market. To investigate the key challenges, we used a mixed-method research approach which includes both a large-scale survey of 5389 responses and an in-depth multiple-case study. The initial findings reveal that thriving in technology uncertainty and acquiring the first paying customer are among the top challenges, perceived and experienced by early-stage software startups. Our study implies deeper issues that early-stage software startups need to address effectively in validating the problem-solution fit.
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The authors reflect on management of big data by organizations. They comment on service level agreements (SLA) which define the nature and quality of information technology services and mention big data-sharing agreements tend to be poorly structured and informal. They reflect on the methodologies of analyzing big data and state it is easy to get false correlations when using typical statistical tools in analyzing big data. They talk about the use of big data in management and behavior research.
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Context Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present an unique combination of characteristics which pose several challenges to software development activities. Objective This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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Very large datasets, also known as big data, originate from many domains, including healthcare, energy, weather, business, and social networks. Deriving knowledge is more difficult than ever when we must do it by intricately processing big data. Organizations rely on third-party, commodity computing resources or clouds to gather the computational resources required to manipulate data of this magnitude. Although social networks are perhaps among the largest big data producers, the collaboration that results from leveraging this paradigm could help to solve big data processing challenges. Here, the authors explore using personal ad hoc clouds comprised of individuals in social networks to address such challenges.
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The focus of this article is an examination of translation dilemmas in qualitative research. Specifically it explores three questions: whether methodologically it matters if the act of translation is identified or not; the epistemological implications of who does translation; and the consequences for the final product of how far the researcher chooses to involve a translator in research. Some of the ways in which researchers have tackled language difference are discussed. The medium of spoken and written language is itself critically challenged by considering the implications of similar ‘problems of method’ but in situations where the translation and interpretation issues are those associated with a visual spatial medium, in this case Sign Language. The authors argue that centring translation and how it is dealt with raises issues of representation that should be of concern to all researchers.
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Thematic analysis is an approach that is often used for identifying, analyzing, and reporting patterns (themes) within data in primary qualitative research. 'Thematic synthesis' draws on the principles of thematic analysis and identifies the recurring themes or issues from multiple studies, interprets and explains these themes, and draws conclusions in systematic reviews. This paper conceptualizes the thematic synthesis approach in software engineering as a scientific inquiry involving five steps that parallel those of primary research. The process and outcome associated with each step are described and illustrated with examples from systematic reviews in software engineering.
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Agile software development methods have been suggested as useful in many situations and contexts. However, only few (if any) exp e- riences are available regarding the use of agile methods in embedded domain where the hardware sets tight requirements for the software. This development domain is arguably far away from the agile home ground. This paper explores the possibility of using agile development techniques in this environment and defines the requirements for new agile methods targeted to facilitate the development of embedded soft- ware. The findings are based on an empirical study over a period 12 months in the development of low-level telecommunications software. We maintain that by addressing the requirements we discovered, agile methods can be successful also in the embedded software domain.
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Purpose – This paper reports on the results of an investigation into how the software development process is initially established within software product start‐ups. Design/methodology/approach – The study employs a grounded theory approach to characterize the experiences of small software organizations in developing processes to support their software development activity. Using the indigenous Irish software product industry as a test‐bed, the authors' examine how software development processes are established in software product start‐ups and the major factors that influence the make up of these processes. Findings – The results show that the previous experience of the person tasked with managing the development work is the prime influencer on the process a company initially uses. Other influencers include the market sector in which the company is operating, the style of management used and the size and scale of the company operations. Originality/value – The model has particular implications for start‐up software product organisations that wish to successfully manage their product development from an early stage.
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Abstract,Case study is a suitable research methodology,for software engineering,research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims,at providing,an introduction to case study methodology,and,guidelines for researchers,conducting,case studies and,readers studying,reports of such,studies. The content is based on the authors’ own,experience from conducting,and reading case studies. The terminology,and,guidelines are compiled,from,different methodology,handbooks,in other research domains, in particular social science and information systems, and adapted to the needs,in software,engineering. We,present,recommended,practices for software engineering,case studies as well,as empirically,derived,and,evaluated,checklists for researchers and readers of case study research. Keywords,Casestudy.Research methodology.Checklists .Guidelines
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The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems (IS) discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies.
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Software startups have long been a significant driver in economic growth and innovation. The on-going failure of the major number of startups calls for a better understanding of state-of-the-practice of startup activities. With a focus on engineering perspective, this study aims at identifying the change in focus of research area and thematic concepts operating startup research. A systematic mapping study on 74 primary papers (in which 27 papers are newly selected) from 1994 to 2017 was conducted with a comparison with findings from previous mapping studies. A classification schema was developed, and the primary studies were ranked according to their rigour. We discovered that most research has been conducted within the SWEBOK knowledge areas software engineering process, management, construction, design, and requirements, with the shift of focus towards process and management areas. We also provide an alternative classification for future startup research. We find that the rigour of the primary papers was assessed to be higher between 2013-2017 than that of 1994-2013. We also find an inconsistency of characterizing startups. Future work can focus on certain research themes, such as startup evolution models and human aspects, and consolidate the thematic concepts describing software startups.
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The context we address is digitalisation and we want to make the case for decision analytics as one of the key drivers to both meet the challenges from big data/fast data and to work out the new possibilities we are getting to mobilise knowledge, i.e. to make tacit knowledge explicit and to make it accessible and usable for automated, intelligent systems. The use of powerful, intelligent systems is one of the relevant solutions in the digitalisation that is now spreading in industry and business. Digitalisation brings increasing competition, slimmer margins for productivity and profitability and more pronounced requirements for effective planning, problem solving and decision making. This requires a transfer of knowledge from experts and experienced people to novice system operators-and to automated, intelligent systems-a transfer we call knowledge mobilisation. We will work out reasons for why digital coaching will be a key part of knowledge mobilisation and a key step in the development of instruments we need for the progress of digitalisation.
Conference Paper
The power of big data and their applications are evident through the enormous attention they have received over the past few years, with the majority of the research focusing on solving technical and business problems. However, the challenge remains on how to harness the potential of big data in order to come up with innovative solutions on various societal problems. Big data have the potential to change the way that entrepreneurs as well as the other stakeholders of a social ecosystem take decisions , and develop new paths to create social innovation by taking data-driven decisions. Nonetheless, there is limited understanding on how social ecosystems need to change to embrace the advancement that big data entail. There is a need to institutionalize innovation through big data and social entre-preneurship, and examine how to successfully exploit big data towards achieving social good and sustainable change. We suggest building on the current state of the art, and go beyond it by merging and extending the findings with insights from the different stakeholders involved in the social innovation process. Further, we propose developing and testing a framework of best practices, that will be used as a roadmap by interested parties to successfully employ big data for social innovation, through the development of prototype applications which will clearly showcase the impact of big data on addressing societal challenges. This position paper concludes with research questions and challenges for future studies in the area.
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The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.
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An evaluation of recent industrial and societal trends revealed three key factors driving software engineering's future: speed, data, and ecosystems. These factors' implications have led to guidelines for companies to evolve their software engineering practices. This article is part of a special issue on the Future of Software Engineering.
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Experts share their views on the growing importance of business analytics and big data for modern business organizations. Information processing has become increasingly more powerful and flexible, with faster and higher-capacity storage and networks. Globalization and other competitive factors have exerted strong pressures to improve efficiencies and effectiveness, and to strengthen business and customer relationships. Each successive stage of this competition requires more data and more analysis to support strategic, managerial, and operational decision-making. This competition is driving the need for more and better analytics technology, which helps to make competition even more intense. This cycle results in a confluence of competitive imperatives and technological advancements that interact in a unique way.
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Many software startups and research and development efforts are actively trying to harness the power of big data and create software with the potential to improve almost every aspect of human life. As these efforts continue to increase, full consideration needs to be given to the engineering aspects of big data software. Since these systems exist to make predictions on complex and continuous massive datasets, they pose unique problems during specification, design, and verification of software that needs to be delivered on time and within budget. But, given the nature of big data software, can this be done? Does big data software engineering really work? This article explores the details of big data software, discusses the main problems encountered when engineering big data software, and proposes avenues for future research.
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Information technology has arguably been one of the most important drivers of economic and social value in the last 50 years, enabling transformational change in virtually every aspect of society. Although the Information Systems community is engaged in significant research on IT, the reach of our findings may be limited. In this commentary, our objective is to focus the IS community's attention on the striking transformations in economic and social systems spawned by IT and to encourage more research that offers useful implications for policy. We present examples of transformations occurring in four distinct sectors of the economy and propose policy-relevant questions that need to be addressed. We urge researchers to write papers based on their findings that inform policy makers, managers, and decision makers about the issues that transformational technologies raise. Finally, we suggest a new outlet to publish these essays on the implications of transformational informational technology.
Conference Paper
Software engineering is not only about technical solutions. It is to a large extent also concerned with organizational issues, project management and human behaviour. For a discipline like software engineering, empirical methods are crucial, since they allow for incorporating human behaviour into the research approach taken. Empirical methods are common practice in many other disciplines. This chapter provides a motivation for the use of empirical methods in software engineering research. The main motivation is that it is needed from an engineering perspective to allow for informed and well-grounded decision. The chapter continues with a brief introduction to four research methods: controlled experiments, case studies, surveys and post-mortem analyses. These methods are then put into an improvement context. The four methods are presented with the objective to introduce the reader to the methods to a level that it is possible to select the most suitable method at a specific instance. The methods have in common that they all are concerned with quantitative data. However, several of them are also suitable for qualitative data. Finally, it is concluded that the methods are not competing. On the contrary, the different research methods can preferably be used together to obtain more sources of information that hopefully lead to more informed engineering decisions in software engineering.
Conference Paper
Many software product companies fail to achieve a worthwhile return on the investments of their financiers, founders and employees. Failures of execution in sales, marketing and delivery are commonly recognized, but failures in product development are less obvious. This paper explores the critical product development issues that can lead to company failure. A model for the evolution of product development from startup to maturity is provided, consisting of three phases: startup; stabilization; and growth. Symptoms that can appear in each phase are discussed and the underlying issues analyzed. This enables stakeholders to benchmark their own product companies and avoid product-related company failure. The paper will be of interest to investors funding startup product companies, executives leading them and product development managers.
Startup genome report extra: premature scaling
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  • B L Herrmann
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Marmer, Max., Herrmann, Bjoern Lasse., Dogrultan, Ertan., Berman, Ron., Eesley, C., Blank, S.: Startup genome report extra: Premature scaling. Startup Genome, vol.10. (2011)
Translation: Theory and Practice, Tension and Interdependence
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Giannakos, Michail: Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management
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Mikalef, Patrick., Pappas, Ilias O., Krogstie, John., Giannakos, Michail: Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, pp.1-32. Springer (2017)
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  • C Ungerer
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