The breakthrough of Distributed Ledger Technologies (DLT) has enabled the emergence and implementation of a wide range of digital platforms in Virtual Enterprises (VE) which collaborate to provide digital services. DLT has the potential to revolutionize VE by offering transparent, decentralized, trustworthy, data provenance, reliable, and auditable features. Yet, the full deployment of DLT systems and digital platforms is still limited since some systems are operating in isolation. Hence, DLT interoperability is one of the challenges inhibiting widespread adoption of DLT platforms. DLT interoperability represents the ability for one distributed ledger platform to interact and share data with other legacy digital applications. It is inevitable to orchestrate these digital platforms fragments by introducing a cross-DLT platform integration to govern data usage within VE. Presently, already proposed approaches for DLT interoperability such as naive relay, sidechain, oracle solutions notary scheme, or relay chain are mostly not employed as they are either resource-intensive or too expensive to operate. Therefore, this paper presents a layered architecture that aids interoperability of DLT, and digital platforms based on IOTA Tangle. Design science method is adopted, and case demonstration is carried out to show how IOTA Tangle enable VE to provide an innovative virtual asset payment platform for seamless electric mobility as a service to clients. IOTA was employed as the DLT platform due to its data traceability, immutability, and tamper-proof features which allow for verification of integrity of data. IOTA offers flexibility and performance to support a reliable digital solution. Findings from this study presents a layered architecture that aids IOTA Tangle to make requests, inter-communicate, and share data via RESTful application programming interface as gateway with other external digital platforms deployed by VE to achieve an interoperable eco-system.
Digital transformation is of crucial importance in the manufacturing industry, especially after the COVID-19 pandemic because of the increasing need for remote working and socially distanced workplaces. However, there is a lack of a clear and well-defined process to implement digital transformation in manufacturing. This paper aims to identify the most critical stages to implementing digital transformation in the manufacturing sector. Twenty-one structured interviews with experienced specialists in digitalisation in the manufacturing sector in the Egyptian economy were held and used the Best–Worst Method to analyse the data as an analysis tool for a multiple criteria decision making (MCDM) approach. The digital transformation process comprises eight stages covering technology, management, communications, and customer elements. The main contribution of this work stage is the balance between the different elements of digital transformation—digital technologies, leadership and strategy, people and business processes—to create an integrated 8-step process of digital transformation in the manufacturing sector of developing economies such as the Egyptian economy.
We conceptualize the new phenomenon of the Fractional Chief Information Officer (CIO) as a part-time executive who usually works for more than one primarily small- to medium-sized enterprise (SME) and develop promising avenues for future research on Fractional CIOs. We conduct an empirical study by drawing on semi-structured interviews with 40 individuals from 10 different countries who occupy a Fractional CIO role. We derive a definition for the Fractional CIO, distinguish it from other forms of employment, and compare it with existing research on CIO roles. Further, we find four salient engagement types of Fractional CIOs offering value for SMEs in various situations: Strategic IT management, Restructuring, Rapid scaling, and Hands-on support. The results reveal similarities with existing CIO roles as well as novel insights concerning the different engagement types. Lastly, we propose a research agenda for the Fractional CIO field, based on four research themes derived from existing CIO research and insights from the interviews.
Far-reaching decisions in organizations often rely on sophisticated methods of data analysis. However, data availability is not always given in complex real-world systems, and even available data may not fully reflect all the underlying processes. In these cases, artificial data can help shed light on pitfalls in decision making, and gain insights on optimized methods. The present paper uses the example of forecasts targeting the outcomes of sports events, representing a domain where despite the increasing complexity and coverage of models, the proposed methods may fail to identify the main sources of inaccuracy. While the actual outcome of the events provides a basis for validation, it remains unknown whether inaccurate forecasts source from misestimating the strength of each competitor, inaccurate forecasting methods or just from inherently random processes. To untangle this paradigm, the present paper proposes the design of a comprehensive simulation framework that models the sports forecasting process while having full control of all the underlying unknowns. A generalized model of the sports forecasting process is presented as the conceptual basis of the system and is supported by the main challenges of real-world data applications. The framework aims to provide a better understanding of rating procedures and forecasting techniques that will boost new developments and serve as a robust validation system accounting for the predictive quality of forecasts. As a proof of concept, a full data generation is showcased together with the main analytical advantages of using artificial data.
Design science is a recognized information systems research paradigm, which is fundamentally centered on problem solving through technology design. The design process involves reflexive thinking and exploration and is usually supported by a variety of visual artifacts, which facilitate structuring, combining, and communicating design knowledge. Visual artifacts are among possible main contributions of a design science endeavor. In this study, we analyze the nature and purpose of such visual artifacts. We adopt semiotics and a theory of visualization of thought, in combination with a literature review, to elaborate a framework of design science visual artifacts. We consider three domains of analysis: intentionality, form-and-function, and visual scheme. We demonstrate the applicability of the framework using two examples. Finally, we define a set of properties that researchers should consider when creating and using visual artifacts in design science: transparency of the relationship between representation and object, self-sufficiency of the visual artifact, and consistency of knowledge communication. The proposed framework helps researchers understand what properties should be focused on when developing their visual artifacts.
O2O (Online to Offline) on-demand service platforms have grown significantly. With the intensive competition on platforms, effective promotion strategies are the key to the success of O2O stores. Given the specific features of O2O platforms and mobile apps, how different promotions strategies work remain a question. The study investigates the effectiveness of different promotion strategies and their interactions on O2O platforms. Using daily sales data from a store on a well-known O2O platform, we construct a product-level panel with daily sales and promotion information. With fixed-effect regressions and GMM models, we find that both discount promotions including price discounts and order-level discounts and display-related promotions including display banners and rankings in lists can significantly increase product sales. We find that top rankings may be one of the most effective promotion strategies in O2O context. Seemingly organic rankings in lists is more effective than large store-page banners. We also find that direct price discounts perform better than order-level discounts for reaching a threshold. Price discounts and display-related strategies strongly complement each other to increase sales, while the impact of order-level discounts cannot be strengthened with display-related promotions. The findings reveal interesting patterns of how different promotion strategies affect sales and interact with each other and provide theoretical and practical implications.
Matching subsidies, through which third-party institutions provide a dollar-for-dollar match of private contributions made through selected campaigns, have served as effective tools to boost fundraising. We utilize a quasi-experiment on a prosocial crowdfunding platform to examine the effectiveness of matching subsidies in shaping funding outcomes and lender behaviors. Although matching subsidies offer matched loans competitive advantages over unmatched loans, we find that total private contributions made to both matched and unmatched loans increase compared to their prematching counterparts, suggesting a positive spillover effect on unmatched loans. However, matching subsidies lead to decreased private contributions made on the platform after a matching event, revealing an intertemporal displacement effect on existing loans. Furthermore, we find that matching subsidies effectively encourage previously inactive lenders to contribute to matched loans, leading to a motivational crowding-out effect on active lenders’ contributions to unmatched loans. These findings shed new light on the overall effectiveness of matching subsidies provided through online crowdfunding platforms.
We investigate how government subsidies affect pricing and service-quality strategies under different online-recycling channel structures. We consider two cases: the monopoly case, where the manufacturer recycles by itself, and the coopetition case, where a platform and the manufacturer compete for used products while the platform provides services to the manufacturer. We find the optimal price and service-quality strategies in these two cases with or without government subsidy. We also examine the recycling outcomes in terms of total recycling quantities and the parties’ profits. We find that with government subsidy, in the monopoly case, the manufacturer is motivated to increase both acquisition price and service quality and thus achieves higher recycling quantities and profit. In the case of coopetition, the manufacturer enjoys similar benefits from subsidy, but the platform suffers. The manufacturer gains competitive advantage from subsidy by offering a higher acquisition price, which forces the platform to increase acquisition price as well. To compensate for this increased cost, the platform must lower service quality, which leads to lower recycling quantities and profits. Only when the subsidy is high enough can the platform benefit. We also show that a subsidy can increase the total recycling quantity of the system only when consumers are fairly insensitive to service quality. Our study contributes to the understanding of how subsidy policy interacts with different online channel structures.
Along with the rapid growth of online and mobile shopping, a recent interesting phenomenon is the introduction of money market funds by many ecommerce platforms. The goal might be to provide consumers the one-stop convenience of both shopping and short-term investment. So far, little rigorous work has examined the relationship between online shopping and investment in ecommerce money market funds (eMMFs). In this study, we examine how consumers’ online-shopping expenditure affects their eMMF investment amounts using data from the China Household Finance Survey (CHFS) dataset. We find that consumers’ online-shopping expenditure increases their eMMF investment amounts, holding other variables constant. This effect is significant and positive even after we consider the potential endogeneity issues with seemingly unrelated regression estimation. Further, analyzing whether consumers’ risky-market experience could moderate this effect, we find the coefficient of the moderating term to be significant after we consider the potential endogeneity issues. These findings suggest that consumers’ eMMF investments is largely affected by their online-shopping experience, and this effect is even stronger for those with risky-market participation.
The digital economy has brought about multi-sided platforms as superior configurations for value co-creation. However, the academic discourse on platforms is scattered across academic disciplines—including management, information systems, and economics. Based on a systematic literature review of 140 papers from nine disciplines, we inductively develop a framework that provides a conceptual point of reference for conducting boundary-spanning research on digital multi-sided platforms. Systematizing the identified concepts, we introduce three layers of abstraction: conceptualizing platforms as information systems, as systems for actor engagement, or as ecosystems. Our framework conceptualizes digital multi-sided platforms as nested hierarchies of systems that are shaped by, and in interaction with, their environment. This view focuses on designing IT artifacts, governance mechanisms, and strategies for platforms in terms of how they interact with their environment. Practitioners can use our insights to analyze, design, and manage platforms aimed at establishing a sustainable competitive advantage.
The popularity of Osterwalder's (2004) business model canvas has led researchers to develop analogous artifacts in a variety of domains. However, we still lack a conceptual foundation explaining the essential and common characteristics of the canvas, regarded as domain-independent artifact. In this study, we focus on how the canvas helps representing and theorizing about a particular behavior or structure. We develop a framework for canvas design, which characterizes the canvas design at the surface and deep levels. While the surface level concerns a lightweight representation, using components and implicit relationships, the deep level involves theorizing about a particular behavior or structure, using a systems perspective and considering a static and a dynamic view of the canvas. The proposed framework is demonstrated in a case addressing the design of the research contribution canvas. This study contributes to a domain-independent conceptualization of the canvas, which can be used to design canvases in various domains.
The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
The platform economy has generated various new and highly successful business models. However, certain models facilitate tax evasion for service providers on their income earned on these platforms. While tax evasion contradicts the pro-social claim of many sharing platforms, it is unclear whether a provider’s tax honesty constitutes a value for consumers at all. This study investigates the role of tax compliance for platform users by employing an online experiment ( $$n=286$$ n = 286 ). The results indicate that consumers perceive providers’ tax compliance and consider it as a trust-enhancing signal. In further analysis, we find that consumers’ moral norms moderate both the signal’s trust-building effect as well as the relation between trust and transaction intention. In light of recent policy debates around taxing the platform economy, this study provides valuable practical insights for tax legislators.
Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.
Service providers compose services in service chains that require deep integration of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-enabled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individually negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management systems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analytics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-supported SLA analytics across organizations.
While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.
For almost 30 years, the way of building business process management maturity models (BPM MMs), the importance assigned to individual maturity levels, and the criteria and critical success factors chosen for BPM maturity assessment have not changed significantly, despite the fact that during those three decades, the business environment and organizations themselves have changed enormously. The impact of hyperautomation and the increasing pace of change require the integration of maturity assessment with the BPM implementation methodology, including the repetition of maturity assessment for selected groups of processes. This causes an urgent need to adapt both process maturity assessment methods and BPM MMs to changing working conditions and business requirements. This conceptual paper is based on a model approach. The framework presented in the article continues and at the same time clearly deviates from the tradition of building BPM MMs on the basis of the Capability Maturity Model (CMM). It proposes a two-stage comprehensive process of organizational process maturity assessment, fully integrated into the process of BPM implementation and further business process management. The presented framework makes it possible to assess the process maturity of Industry 4.0 organizations in which dynamic knowledge-intensive business processes (kiBPs) play a key role in creating value.
Although the average tenure of CIOs has increased over the last years, the majority of CIOs have been in their positions for only three years or less. Nevertheless, some CIOs have been successful in their position for a long time. In this study, we use tenure as a proxy for success as a CIO. The goal of this paper is to examine factors that are critical to the success of long-term CIOs. For this purpose, we created and analyzed resumes of 384 CIOs. Out of these 384, we conducted 19 interviews with CIOs from top-tier companies and collected and analyzed both qualitative and quantitative data. In the process, we were able to identify nine factors that are critical for the success (CSF) of CIOs. These factors fall into three categories. Category “Personality” includes “Accepting and embracing change” (CSF #1), “Being perseverant to pursue long-term goals” (CSF #2), “Anticipating the future through visionary thinking” (CSF #3), and “Being empathetic to deal with uncertainty felt by co-workers” (CSF #4). The “Role Fulfilment” category includes “Cross-functional involvement and integration of the IT organization” (CSF #5), “Positioning and restructuring of the IT organization” (CSF #6), and “Well-connected and communicative leadership” (CSF #7). The “Organizational Environment” category consists of “Availability of skilled workforce” (CSF #8) and “Reporting line to the CEO” (CSF #9). CSFs 1, 2, and 3 were perceived as most important by the participating CIOs. The results may be of particular interest both to aspiring CIOs and equally their employing organizations, as they reflect what long-term CIOs value during their time in office.
In the past decade, cities around the world have published their open data as a new service. Some have used this service innovatively, as a vehicle to improve service quality, efficiency, accountability, and transparency. While some studies highlight open data success stories, others show a lack of supporting evidence. In either case, cities currently face such challenges as: How can cities and organizations measure their open data capability? How does assessment allow enhancement of open data capability? The purpose of our study is to demonstrate applied open government data evaluation using the theoretical lens of dynamic capability. An assessment based on a framework called Open Data Roadmap (ODR) is performed on Denton, TX, a mid-sized U.S. city. Data is then integrated through a mixed-method analysis involving two publicly available OD models (CODC and Thorsby, respectively) and a crowdsourced resident evaluation, and the results are presented of the effects of this assessment in relation to dynamic capabilities involving a third model (Chong et al. 2018). We conclude that ODR has the potential to assist governments and other organizations in boosting community engagement, introducing innovative services, and presenting OD as one component of their organization’s competitiveness.
This study aimed to explore the organizational resources, competencies, and capabilities needed for the successful implementation of machine learning development projects for digital marketing operations in marketing organizations. The structure of the machine learning development project was investigated via the Agile-Stage-Gate model to identify the workflow, tasks, and roles of the marketing management and development teams during the project. With the accomplished project illustration, the necessary resources, competencies, and capabilities were identified. The findings suggest that marketing organizations’ capability to understand and refine data by taking into the notion the impact of the marketing environment is the most crucial competence of machine learning development projects because it forms a solid base for algorithm execution and successful project implementation for marketing purposes. Marketing organizations must develop rigorous business processes and management procedures to support data governance and thus provide suitable data for machine learning purposes. Personnel’s understanding of the data’s characteristics and capabilities for running successful machine learning projects were also seen as key competencies for marketing organizations.
Electronic markets have grown substantially, and they are considered an effective form of retail in recent years. Despite such growth, lack of physical transactions between different parties, as well as users' concerns about their privacy and security of transactions in electronic commerce (e-commerce) platforms have jeopardized users' trust. Thus, trust as a key issue for reducing consumers' perceived risk and the successful promotion of e-commerce has motivated many researchers to study it. This paper created a comprehensive and up-to-date framework that synthesized the previous studies in the literature conducted on trust in e-commerce environments. A systematic literature review method was selected to achieve this aim. The initial search in 17 top-ranked information systems journals and conferences resulted in 129 papers that met the inclusion criteria. Then these studies underwent an in-depth examination to determine how trust had been conceptualized in e-commerce environments. Further, the theoretical bases in relation to trust in e-commerce contexts used in the literature were investigated. The study concludes with implications for practice and a critical agenda for future research.
Companies need to be able to demonstrate compliance with rules and regulations, especially start-ups who typically do not have the legal expertise to identify, assess and address legal risks of initial business ideas, nor do they have the resources to hire such expertise. Tools could help them identify and deal with legal risk at an early stage. Existing research in BPM focuses on compliance verification of a consolidated business model by checking the ability of a company to comply with the standards. The challenge is to apply a ‘continuous improvement’ by steering the business on values. Moreover, legal choices typically sit at the strategic level, and not only at the operational level. In this paper, we therefore propose an approach to handle legal risks as part of business model development. The approach makes use of Continuous Business Model Planning method, a value-driven modeling approach for strategic planning, and legal argumentation. The suitability and potential usefulness of the approach is illustrated by a study of the Kenyan court case Lipisha & BitPesa vs. Safaricom.
With the increasing adoption of robotics in professional applications, the question arises of what impact robots with more cognitive skills will have on the labor market. Since such intelligent robots can highly affect the way of doing business, prior studies have mainly targeted their economic impact on work productivity. This article, on the other hand, focuses on the acceptance of intelligent robots by employees. We conducted 48 semi-structured interviews with office workers and managers. Based on three dilemmas, this paper uncovers why such employees would leave their work practices (fully or partially) to intelligent robots. Our findings show that many tasks can already be replaced with the necessary support. Employees seem highly positive about robots in the workplace and feel comfortable leaving simple tasks. Since they are especially skeptical about using robots for social, creative or confident tasks, proper guidance and training are crucial. By looking at the human level of intelligent robots, we add social and ethical considerations. Organizations gain insight into how employees typically view robotic changes to proactively react to employee concerns by gradually adopting their corporate innovation strategy. This study also provides an impetus for further research, with the ultimate aim of humanizing digital work.
The increasing adoption of three-dimensional (3D) virtual reality technologies in business practices requires an interdisciplinary research approach to study their effect. In this paper we investigate the effects of different 3D online store layouts on user perceptions in the e-retailing context. We build on recent research on store atmosphere that classifies store layouts in 3D environments as “avant-garde”, “warehouse”, “pragmatic”, “boutique” and “department”. Reflecting the dual identity of individuals as both consumers visiting virtual stores and users interacting with graphical user interfaces, we employ key constructs from both the marketing and the information systems literature to build our research model. The study measures how Perceived Usefulness, Perceived Ease of Use, Merchandise Quality Perception and Store Perception vary across the distinct store layouts. We employ a laboratory experiment in the apparel industry to test our model. Our results show that the layouts lead to different perceptions, although the consumers’ Shopping Motivation does not moderate this effect. Building on the differences found on store layout effects on user/consumer behavior in the 3D online context, the paper discusses relevant research and practical implications.
The digital transformation, with its ongoing trend towards electronic business, confronts companies with increasingly growing amounts of data which have to be processed, stored and analyzed. Instant access to the “right” information at the time it is needed is crucial and thus, the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage. In this context, one important field of application is digital marketing, because sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior based on their reviews, complaints as well as posts in online forums or social networks. However, existing tools for the automated analysis of social content often focus on one general approach by either prioritizing the analysis of the posts’ semantics or the analysis of pure numbers (e.g., sum of likes or shares). Hence, this design science research project develops the software tool UR:SMART, which supports the analysis of social media data by combining different kinds of analysis methods. This allows deep insights into users’ needs and opinions and therefore prepares the ground for the further interpretation of the voice. The applicability of UR:SMART is demonstrated at a German financial institution. Furthermore, the usability is evaluated with the help of a SUMI (Software Usability Measurement Inventory) study, which shows the tool’s usefulness to support social media analyses from the users’ perspective.
Business model innovation (BMI) describes the efforts made by the business in finding new business logic or new ways of value creation. Technological change is deemed to be the main driver of BMI. This study focused on the emergence of the internet of things (IoT) as a technological driver of BMI in internet service providers’ (ISPs) business context, in the scope of wired access (WA) and fixed wireless access (FWA) providers, and addressed new ways of value creation for ISPs driven by IoT. To this end, a four-stage BMI process, including; initiation, ideation, integration, and evaluation, was used. In the implementation of the BMI process, we used the data extracted from the literature of IoT, BMI, and ISP business, as well as those obtained through interviews with experts. As a result of the process implementation, we identified possible ideas for the value creation of ISPs in the IoT domain, based on connectivity service providing, cloud service providing, technical solution providing, and business solution providing. Then, we proposed ISPs’ business models in the IoT domain, in accordance with the identified ideas, based on Hedman and Kalling’s ontology. To boost the validity of the proposed business models, the stress testing approach was recruited at the final stage of the BMI process. Implementing BMI, driven by IoT, in the ISPs’ context, reduces constraints imposed by the paucity of knowledge in both BMI and IoT, helps ISPs’ managers to anticipate and identify the IoT-based opportunities, and provides a starting point for further studies on new ways of value creation in other businesses in the telecom industry.
In our research, we suggest a process theory for explaining the strategy assessment process and its effect in information systems (IS) planning. The proposed theory is derived from an analysis of practitioners and the practices they employ. Based on a multiple-case study design, we look at the IS management teams of three corporate IS departments and how they prepare for strategy development. The analysis of the projects reveals a stable pattern of activities employed by the three teams to assess their departments' strategic positions and existing strategy. Along with this procedural understanding, our research also produces a detailed look at the outcomes of these managerial practices.
Digital transformation affects all industries. This study targets how management consulting companies address this phenomenon. Based on a survey of 30 Romanian management consulting companies and a qualitative comparative analysis, we model the relationship between management consulting companies’ current context (customers’ industries, internal and external triggers for digital transformation), the current state of digital transformation, and expected digital transformation. By considering managerial expectations importance in future decisions, and contingency theory for explaining the links between context, current state and expected digital transformation, different paths concerning digital transformation are identified at Romanian management consulting companies. For some of them, the combination of internal and external triggers and the existence of previous digital transformation activities led to increases in the recognized importance of digital transformation in future business models and to newer business services (digital strategy). For others, which do not have powerful external triggers, digital transformation is associated with internal efficiency–related triggers, and it targets only improvements in existing business models due to technology adoption (technology strategy). A small number of management consulting companies do not expect digital transformation to have a large impact on their future business model, because of either the lack of external triggers to do so or the absence of previous digital transformation activities. This research demonstrates the contingency and evolutionary nature of the digital transformation process, in which specific combinations between internal and external triggers can explain the expectations of management consulting companies’ managers about digital transformation.
While there is a plethora of literature on IT Sourcing (ITS) strategy, little is known about the impact of large-scale agile frameworks on these strategies. Empirical evidence suggests that application of agile frameworks has an impact on governance and processes in large organisations including ITS strategies. Yet, the effects of such frameworks remain unrevealed. This research investigates the impact of agile frameworks on ITS decisions and the way organisations configure their ITS strategies. The research first studies literature to realise that there is a lack of empirical research on ITS strategies in organisations that use agile frameworks. Then, through a systematic literature review, ten different dimensions of ITS are identified and used as the required construct for a multiple-case study at six Netherlands-based organisations. The results reveal that four dimensions, namely sourcing model, location, pricing model, and relational governance are mostly affected by agile frameworks. Furthermore, after more than three years of utilising agile frameworks, case organisations still have not discovered a proper optimum point for these dimensions. The results also uncover that organisations are not fully aware of the impact of agile transformation on the process of ITS decision-making. This process may remain intact for years, resulting in continuous experimentation and trial and error of ITS strategies. We conclude that organisations should recognise the effects of agile frameworks to make ITS decisions accordingly. Additionally, adhering to a more rational and structured decision-making process helps organisations to more efficiently find proper optimum points for the dimensions of ITS strategy.
With the rapid changes in the global business environment, enterprises face many risks for their supply chains. The development of blockchain technology (BCT), an emerging technology, could transform supply chain activities and provides an opportunity to mitigate supply chain risks (SCRs). However, there is a lack of guidance on this issue. Therefore, this study evaluates and prioritizes the impacts of BCT on reducing SCRs. By applying the analytic hierarchy process (AHP) approach, this study identifies and assesses the nineteen BCT adoption enablers for reducing SCRs in a hierarchical structure. Results show that the sourcing process is the essential enabler when enterprises consider adopting BCT for reducing risks, and the weight of the making process is ranked second. Among the sub-criteria, the top five items are clarity of supply sources, counterfeit and shoddy products, fraud in contract fulfillment, the flexibility of capacity, and sensitivity to demand change. This study's findings may guide practitioners considering the introduction of BCT to reduce SCRs and for scholars who are developing related theories.
Blockchain technology has emerged as an important research domain in recent years. It not only supports the secure and efficient storage and processing of information but may also transform the business principles and processes embedded in traditional centralized organizations and societies. This editorial first provides a framework that identifies the emerging areas of blockchain research. The key characteristics of this framework in Blockchain 1.0, 2.0, and 3.0 are defined and introduced. The impacts and opportunities associated with blockchain research are identified and discussed. At last, the six articles in this special issue are characterized using the proposed research framework of blockchain research.
The existence of different stakeholders in a system, the actual and potential contradictions that may not be identified, and the long-term consequences of each decision are significant challenges in the process of developing a system. Information technology-based services are among the systems that usually interact with users and have a significant impact on their environment. The present study attempts to properly understand and develop the services of an intercity payment system in Iran. After recognizing the problem step by step, an innovative methodology was designed to structure the problem. In the early stages, using one of the critical frameworks after identifying stakeholders, research on the system was conducted, and interviews were identified, which are involved and affected people by the system. Critiques of stakeholders became to viable option during the specified process. Additionally, inspired by the soft operational research (OR), the obtained results were examined in terms of the executive contradictions, and then to evaluate and select the correct options, the long-term consequences of each decision were identified using Multi Attribute Decision-Making. Moreover, the evaluation of options for different stakeholders was done by their representatives using their related criteria to select the best options from the opposing options. Ultimately, the presented methodology is designed to provide a slow movement from qualitative to quantitative form. Consequently, based on creating shared value, the product has been redesigned to satisfy all stakeholders.
The implementation of augmented reality (AR) systems in production environments is associated with a variety of advantages, such as productivity gains, lower costs and reduced operating times. Despite these potential benefits, the lack of user acceptance due to issues such as privacy concerns constitutes a barrier to diffusion in workplace environments. In order to better understand the issues surrounding AR acceptance, we employed a conjoint study to empirically examine the trade-offs that future employees perceive when being involved in adopting such systems. Using a hierarchical Bayes estimation, we discover that functional benefits such as productivity gains and safety enhancement are the main adoption drivers. In contrast, future employees indeed perceive monitoring through head-worn AR devices as negative. However, a complementary cluster analysis indicates that not all respondents share a negative view of monitoring, and one third are likely to share their performance data with employers. We identify three groups with significantly different utility patterns. Furthermore, we monetize the value of privacy to determine compensation payments. The results may help employers, decision-makers, software solution providers as well as researchers in the information systems domain to better understand the factors surrounding acceptance of AR assistance systems. To the best of our knowledge, we are the first to address this issue using conjoint analysis.
Increasingly, universities across the globe are involved in collaborations at both national and international levels. In a nutshell, the collaborations are intended to expose students and academia to different environments, to facilitate enhanced teaching and learning, and research activities. However, many of the collaborative initiatives have not been successful, particularly at international level. This can be attributed to many factors, which are either unknown or too complex to address by the drivers of collaborations in many universities and countries. As a result, various approaches such as physical exchange of materials and humans (students, professors, and other university staff) have been adopted over the years, yet the success rate has not improved. The objective of this research was to develop a model, which can be used to guide an understanding of how to employ global virtual teams for university’s collaborations. The qualitative methods from the perspectives of the interpretive stance and inductive approach were employed. Data were gathered from literature, and an existing collaboration, which involves universities from three different continents, Africa, Europe and North America. The data were analysed following the hermeneutics technique, from the interpretivist perspective. From the analysis, we found three sets of factors: human sphere, collaborative activity and technology artefacts as the main facilitators and influence on universities collaborations. Based on the findings, a global virtual team model was developed, which can be useful in guiding and advancing the way in which team members interact and enable activities of collaborations between universities, and company as well.
This study integrates consumer innovativeness (CI) and technological expertise (TE) in consumer attitudes and mobile commerce use (MCU) and introduces consumer consideration set size (CSS) as a moderator and a mediator of these relationships. Based on a survey sample of 577 Vietnamese consumers, it uses a structural equation modelling approach to test the hypotheses. The findings show that attitudes, CI, TE, and CSS have direct positive effects on MC use. The results also indicate a significant moderation effect between CI and TE on MCU. In particular, this study demonstrates that CSS is an important moderator and mediator in the relationships between attitudes, CI, TE, and MCU. The inclusion of mediation effects sharply increases the explained variance of MCU from 25.0% to 37.3%, with an effect size of 49.2%, compared with the model that only includes the direct effects. When the moderator effects are added, the explained variance of MCU increases to 51.7%, with an effect size of 38.6%, compared with the mediation model. Thus, the inclusion of mediation and moderation extends our understanding of the innovativeness–attitude–behaviour relationship in explaining MCU. A deeper understanding of the size and structure of the consideration set is essential to obtain a higher consumer adoption rate and increase loyalty, especially for innovative consumers with high TE.
The realisation of citizen-centric services in the public sector requires breaking traditional silos and transforming existing institutional structures and processes. Recent transformation efforts undertaken in government institutions have embraced business process re-engineering (BPR) concepts championed by the private sector over decades ago to facilitate such change. While public opinion continues to differ about these transformation efforts' success, there is little evidence to explain the influence of BPR on their success or failure. This paper explores BPR led public sector transformation efforts in two local authorities in Europe to evaluate the outcomes realised for both government and citizens. Empirical evidence reveals that while transformation efforts contributed towards improving efficiency and integrating processes across functions in the public sector, the institutional structures evolved into a collection of reshaped and newly formed siloes, which were distinctly focused on delivering a citizen-centric service.
Arrival of blockchain is set to transform supply chain activities. Scholars have barely begun to systematically assess the effects of blockchain on various organizational activities. This paper first summarizes the research articles published between year 2015 and year 2018 into four areas: hardware, software, emerging technology, and business applications. The paper then discusses the key enabling technologies applied in blockchain and three types of blockchain structures. Finally, seven case studies of blockchain projects in the maritime and shipping industry are discussed. This paper examines how blockchain is likely to affect key supply chain management objectives such as cost, quality, speed, and risk management. The paper illustrates various mechanisms by which blockchain can help achieve the above supply chain objectives. The paper presents early evidence linking the use of blockchain in supply chain activities to increasing transparency and accountability. Special emphasis has been placed on the degree of deployment of blockchain to validate individuals’ and assets’ identities.
Blockchain technology is predicted to reshape existing business models of the financial services industry. But although blockchain is often seen as a strategic technology, research focusing on its impact on business models is still rare. This research derives a hypotheses model that connects IT innovations with the three generic value disciplines of banks “operational excellence”, “customer intimacy” and “product leadership” as well as the four generic elements of business models “what”, “who”, “how” and “value”. A business model acts as a mediator for IT innovation. To test the hypothesis model data provided from an international survey of 104 financial services institutions and start-up companies was applied. The results support the hypothesis that all three value disciplines might be impacted by blockchain technology in the future. The regression analysis reveals that especially banks’ operations could be significantly changed. With these results, this research contributes to the emerging literature on blockchain and business models and the strategic use of IT.
Smart contracts are seen as the major building blocks for future autonomous blockchain- and Distributed Ledger Technology (DLT)-based applications. Engineering such contracts for trustless, append-only, and decentralized digital ledgers allows mutually distrustful parties to transform legal requirements into immutable and formalized rules. Previous experience shows this to be a challenging task due to demanding socio-technical ecosystems and the specificities of decentralized ledger technology. In this paper, we therefore develop an integrated process model for engineering DLT-based smart contracts that accounts for the specificities of DLT. This model was iteratively refined with the support of industry experts. The model explicitly accounts for the immutability of the trustless, append-only, and decentralized DLT ecosystem, and thereby overcomes certain limitations of traditional software engineering process models. More specifically, it consists of five successive and closely intertwined phases: conceptualization, implementation, approval, execution, and finalization. For each phase, the respective activities, roles, and artifacts are identified and discussed in detail. Applying such a model when engineering smart contracts will help software engineers and developers to better understand and streamline the engineering process of DLTs in general and blockchain in particular. Furthermore, this model serves as a generic framework which will support application development in all fields in which DLT can be applied.
Blockchain technologies have become increasingly popular and attracted great interest in recent years. Beyond the financial sector, blockchain technologies are promising for addressing the current limitations in food supply chain management. As blockchain adoption for food supply chains is still in an early stage, it is significant to have a thematic framework to systematically understand the processes, benefits, and challenges. This paper aims to explore the adoption of blockchain technologies in food supply chains with a thematic analysis. Desktop research is conducted, and data is collected from online databases, including news article (e.g. Factiva) and research paper databases (e.g. Web of Science). Then we carry out a qualitative thematic analysis, according to the investigation processes suggested by Creswell. Based on the thematic analysis, we identify seven first-order themes and two second-order themes in adoption processes; thirteen first-order themes, sixteen second-order themes, and five third-order themes in benefits; and fourteen first-order themes and five second-order themes in challenges. In addition, we discuss and propose how to improve blockchain adoption in food supply chains in practice.
The used car market is characterized by information asymmetries and mistrust. Blockchain technology promises to resolve these problems using a system which stores data over the life cycle of a vehicle. However, while blockchain technology is strong in preserving the stored information, sense-making of this information is still essential to bring value to end consumers of the system. In this paper, we take an exploratory approach and create a prototype, which is then evaluated in realistic car sale conversations between buyers and sellers. We demonstrate and discuss how the interplay of different design elements of an application, built on top of a blockchain-based platform, helps to reduce information asymmetries and enhance trust. Our findings suggest that though providing more information about a used product (a car) leads to fewer information asymmetries in general, a reputation mechanism and data analysis are both beneficial in improving the situation further. As for trust, such a system enhances trust between buyers and sellers and, in general, makes the overall purchase process more trustworthy. However, to achieve these positive effects, the quality of the stored information should be guaranteed and properly communicated to the end-user.
As a new and rapidly developing distributed technology, blockchain has shown substantial impact on the fields of cryptocurrency and e-commerce and thus has attracted the interests of governments, enterprises, and research institutions. As such, it is important to understand the state of blockchain research in order for those institutions to plan their research effectively. Towards this goal, we first integrate two search strategies to construct a representative dataset. Then we analyze the blockchain literature between 2013 and 2018 in this dataset with a science mapping approach and Latent Dirichlet Allocation model. Based on the results of the analysis, we found the following insights: (1) the evolution of blockchain research involves many disciplines while two major disciplines—computer science and business—lead the way. (2) The current research can be divided into four research areas: underlying technology architecture, privacy and security, financial application, and smart scene applications. (3) The evolution of blockchain research has gone through three stages: basic blockchain technology, various business applications, and integration with advanced technologies such as fog computing, Internet of things, and artificial intelligence. (4) We also discovered a close coupling of two research issues that have transformative power, i.e., the potential by the application of cryptocurrency in the real world and the improvement of blockchain technology based on the application requirements. Overall, our study uses multiple complementary scientometric methods to generate a panoramic view of the recent developments in blockchain research. Future research directions and some limitations are discussed.
While there is a plethora of literature on IT Sourcing (ITS) strategy, little is known about the impact of large-scale agile frameworks on these strategies. Empirical evidence suggests that application of agile frameworks has an impact on governance and processes in large organisations including ITS strategies. Yet, the effects of such frameworks remain unrevealed. This research investigates the impact of agile frameworks on ITS decisions and the way organisations configure their ITS strategies. The research first studies literature to realise that there is a lack of empirical research on ITS strategies in organisations that use agile frameworks. Then, through a systematic literature review, ten different dimensions of ITS are identified and used as the required construct for a multiple-case study at six Netherlands-based organisations. The results reveal that four dimensions, namely sourcing model, location, pricing model, and relational governance are mostly affected by agile frameworks. Furthermore , after more than three years of utilising agile frameworks, case organisations still have not discovered a proper optimum point for these dimensions. The results also uncover that organisations are not fully aware of the impact of agile transformation on the process of ITS decision-making. This process may remain intact for years, resulting in continuous experimentation and trial and error of ITS strategies. We conclude that organisations should recognise the effects of agile frameworks to make ITS decisions accordingly. Additionally, adhering to a more rational and struc-tured decision-making process helps organisations to more efficiently find proper optimum points for the dimensions of ITS strategy.
Along with enterprise transformation, enterprise re-engineering is essential for maintaining the competitiveness of an enterprise. Enterprise re-engineering addresses (emergent) changes, re-organizing, outsourcing and re-aligning alike. Re-engineering itself has drawn traction in both academia and business. Most scholarly work in this area is confined to model-driven analysis, holistic frameworks for analyzing as-is/to-be enterprise models, and a few other conceptualization techniques. The practice of process redesign understandably takes the stage in re-engineering. Yet algorithmic techniques that insightfully point out how a process might be improved for proactively re-engineering process-intensive enterprise architecture are missing. Data science and business intelligence have brought a refreshingly new analysis to this mainstream problem by studying the operational history of a business process to facilitate most plausible changes. In this article, we investigate enterprise process redesign taking into account enterprise’s high-level strategy and data warehouse. More specifically, we propose an approach to reasoning about an enterprise’s strategy together with data mining rules extracted from the data warehouse of the enterprise. Our redesign algorithms suggest design-time changes to be made to its business processes, primarily by eliminating redundant tasks and re-ordering inefficiently-located tasks. We analyze the effectiveness of candidate to-be business processes with regard to business intelligence indicators. We report our work on the enterprise architecture developed for a retailer of low-cost domestic flights.
Since online reviews have become an increasingly important information source for consumers to evaluate products during online shopping, many platforms started to adopt review mechanisms to maximize the value of such massive reviews. In recent years, the review tag function has been adopted in practices and leading the research of sentiment and opinion extraction techniques. However, the examination of its impact has been largely overlooked. In this paper, by proposing a framework through the lens of attribution theory, we look into the effect of the review tag function on two focal outcomes. One is the evaluation of highly-rated popular products, the other is the helpfulness perception of product reviews. Experimental methods and qualitative analysis were utilized to test our hypotheses. Our findings demonstrate the importance of tag function application as it further increases consumers’ product evaluation for popular products. We also found that different tag function appearances influence consumers’ cognitive biases in review helpfulness perception.
Contribution of small and medium scale enterprises is significant in development of any country, specifically developing countries like India. Therefore, synchronization and integration of their business processes and functions through effective and efficient information sharing is vital. Hence, enhancing their competence through the adoption of an appropriate Enterprise resource planning (ERP) system is crucial to improve their competitiveness in the competitive scenario. Under this backdrop, this paper analyses pertinent factors for selecting an appropriate ERP system for SMEs.
This mix-method research involved triangulation design by merging both quantitative and qualitative techniques. Factor Analysis was applied for selection of relevant factors affecting ERP procurement decision. Fuzzy Analytical Hierarchy Process approach, based on triangular fuzzy numbers, was used to rank these factors. Further, sensitivity analysis was performed to ensure the robustness of the FAHP results.
Cost of deployment was identified as the most significant criteria. On the other hand, ‘Vendor credibility’ was found to be the least significant factor. Criterion ‘User friendliness and Security’ and ‘Need fulfilment’ were ranked as second and third.
ERP vendors can use these findings in developing appropriate marketing mix strategies. SME owners can also make use of these findings by collaborating with other SMEs for buying an ERP.
This study can be seen as first attempt in investigating and ranking various factors affecting ERP adoption decision for SMEs, specifically in Indian context, using FAHP approach.
The level of viral diffusion expected after a technology product or service is launched is important for determining the marketing budget, forecasting revenue, and understanding the resources necessary to manufacture and/or support the technology. Based on a review of academic and industry literature, this study proposes the “Comprehensive Framework of Viral Technology Diffusion—Stages and Factors.” The framework specifically draws on stage models developed by Wiedemann et al. (Wiedemann DG, Palka W, Pousttchi K (2008) Understanding the determinants of mobile viral effects-towards a grounded theory of mobile viral marketing. Paper presented at the 2008 7th international conference on mobile business) and Phelps et al. (Phelps et al., J Advert Res 44:333–348, 2004), other academic and industry viral marketing literature, and information systems literature including theories widely used in IS research, such as reasoned actioned (Fishbein and Ajzen Fishbein and Ajzen, Intention and behavior: an introduction to theory and research, Addison-Wesley, MA, 1975), Technology Acceptance Model (Davis 1989; Davis et al. 1989) and planned behavior (Ajzen Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In: Action control, Springer. New York, Ajzen, Organ Behav Hum Decis Process 50:179–211, 1991). It details five major stages of viral diffusion campaigns, their relationships to each other, and relevant findings from the literature. The five stages recognized are: content development; virality prediction; seed engagement; content propagation; and virality response. Each stage has been identified in prior studies; however, no comprehensive framework of these constructs, nor their relationships to one another, has so far been found. Some important factors included are, for example, at the propagation stage, consideration of the networks through which viral propagation occur and potential transmission costs faced by content forwarders. Our framework contributes to the literature by providing an outline and related set of factors that may be useful for understanding and positioning future research on viral marketing and may also help practitioners who seek to launch products virally. We also offer directions for future research.