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A Multi-Criteria Approach toward Accelerating for Artificial Intelligence Business Ecosystems: A Perspective of AI Startups CEOs
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Background
Generative Artificial Intelligence (AI) technology, for instance Chat Generative Pre-trained Transformer (ChatGPT), is continuously evolving, and its userbase is growing. These technologies are now being experimented by the businesses to leverage their potential and minimise their risks in business operations. The continuous adoption of the emerging Generative AI technologies will help startups gain more and more experience with adoptions, helping them to leverage continuously evolving technological innovation landscape. However, there is a dearth of prior research on ChatGPT adoption in the startup context, especially from Entrepreneur perspective, highlights the urgent need for a thorough investigation to identify the variables influencing this technological adoption. The primary objective of this study is to ascertain the factors that impact the uptake of ChatGPT technology by startups, anticipate their influence on the triumph of companies, and offer pragmatic suggestions for various stakeholders, including entrepreneurs, and policymakers.
Method and analysis
This study attempts to explore the variables impacting startups’ adoption of ChatGPT technology, with an emphasis on comprehending entrepreneurs’ attitudes and perspectives. To identify and then empirically validate the Generative AI technology adoption framework, the study uses a two-stage methodology that includes experience-based research, and survey research. The research method design is descriptive and Correlational design. Stage one of the research study is descriptive and involves adding practical insights, and real-world context to the model by drawing from the professional consulting experiences of the researchers with the SMEs. The outcome of this stage is the adoption model (also called as research framework), building Upon Technology Adoption Model (TAM), that highlight the technology adoption factors (also called as latent variables) connected with subset of each other and finally to the technology adoption factor (or otherwise). Further, the latent variables and their relationships with other latent variables as graphically highlighted by the adoption model will be translated into the structured questionnaire. Stage two involves survey based research. In this stage, structured questionnaire is tested with small group of entrepreneurs (who has provided informed consent) and finally to be distributed among startup founders to further validate the relationships between these factors and the level of influence individual factors have on overall technology adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) will be used to analyze the gathered data. This multifaceted approach allows for a comprehensive analysis of the adoption process, with an emphasis on understanding, describing, and correlating the key elements at play.
Discussion
This is the first study to investigate the factors that impact the adoption of Generative AI, for instance ChatGPT technology by startups from the Entrepreneurs perspectives. The study’s findings will give Entrepreneurs, Policymakers, technology providers, researchers, and Institutions offering support for entrepreneurs like Academia, Incubators and Accelerators, University libraries, public libraries, chambers of commerce, and foreign embassies important new information that will help them better understand the factors that encourage and hinder ChatGPT adoption. This will allow them to make well-informed strategic decisions about how to apply and use this technology in startup settings thereby improving their services for businesses.
In light of the rise of generative AI and recent debates about the socio‐political implications of large‐language models, chatbots, and the like, this paper analyzes the E.U.’s Artificial Intelligence Act (AIA), the world's first comprehensive attempt by a government body to address and mitigate the potentially negative impacts of AI technologies. The paper critically analyzes the AIA from a business and computer ethics point of view—a perspective currently lacking in the academic (e.g., GBOE‐related) literature. It evaluates, in particular, the AIA's strengths and weaknesses and proposes reform measures that could help to strengthen the AIA. Among the AIA's strengths are its legally binding character, extra‐territoriality, ability to address data quality and discrimination risks, and institutional innovations such as the AI Board and publicly accessible logs and database for AI systems. Among its main weaknesses are its lack of effective enforcement, oversight, and control, absence of procedural rights and remedy mechanisms, inadequate worker protection, institutional ambiguities, insufficient funding and staffing, and inadequate consideration of sustainability issues. Reform suggestions include establishing independent conformity assessment procedures, strengthening democratic accountability and judicial oversight, introducing redress and complaint mechanisms, ensuring the participation and inclusion of workers, guaranteeing political independence of the AI Board, providing enhanced funding and staffing of market surveillance authorities, and mandating “green AI.”
Automation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups.
The concept of innovation ecosystems has become prominent due to its explanatory power. It offers a convincing account of innovation, explaining how and why innovation pathways change and evolve. It has been adopted to explain, predict, and steer innovation. The increasing importance of innovation for most aspects of human life calls for the inclusion of ethical and social rights aspects into the innovation ecosystems discourse. The current innovation ecosystems literature does not provide guidance on how the integration of ethical and social concerns into innovation ecosystems can be realised. One way to achieve this is to draw on the discussion of responsible research and innovation (RRI). This paper applies RRI to the innovation ecosystems discourse and proposes the concept of responsible innovation systems. It draws on the discussion of the ethics of artificial intelligence (AI) to explore how responsible AI innovation ecosystems can be shaped and realised.
Purpose
The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand.
Design/methodology/approach
This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods.
Findings
AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip.
Originality/value
To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.
This article focuses on the broader lessons from mission-oriented programs for innovation policy- and indeed policies aimed at investment-led growth. While much has been written about case studies on missions, this has not resulted in an alternative policy making toolkit. Missions-in the least- require those tools to be just as much about market cocreating and market shaping, as they are about market fixing. The article reviews the characteristics of mission-oriented programs, ooks at key features of those programs that can provide lessons, and discusses how to choose and implement mission-oriented policies, with an example. © The Author(s) 2018. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved.
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
How can entrepreneurial ecosystems and productive entrepreneurship be traced empirically and how is entrepreneurship related to entrepreneurial ecosystems? The analyses in this chapter show the value of taking a systems view on the context of entrepreneurship. We measure entrepreneurial ecosystem elements and use these to compose an entrepreneurial ecosystem index. Next, we measure the output of entrepreneurial ecosystems with different indicators of high-growth firms. We use the 12 provinces of the Netherlands as a test case for measuring the entrepreneurial ecosystem elements, composing an entrepreneurial ecosystem index and relate this to entrepreneurial outputs. The prevalence of high-growth firms relates to the overall value of the entrepreneurial ecosystem index, but not to individual elements of the entrepreneurial ecosystem. The model fit increases once we introduce a multiplicative index and a non-linear model. By measuring entrepreneurial ecosystems and their outputs in this way we move from the ecosystem metaphor to a complex system model of the entrepreneurial economy.
Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that a 'right to explanation' of decisions made by automated or artificially intelligent algorithmic systems will be legally mandated by the GDPR. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive limited information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a 'right to be informed'. Further, the ambiguity and limited scope of the 'right not to be subject to automated decision-making' contained in Article 22 (from which the alleged 'right to explanation' stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.
This chapter presents a theoretical and methodological approach to the study of communication and its effects through the diffusion of innovation from a communication approach. It demonstrates through the history of diffusion research how diffusion of innovations occurs and how it impacts communication outcomes. Based on rural sociology of the 1930s and 1940s, the impact of communication platforms and an "inside out" approach are introduced as ways to test and evaluate communication change through message and source strategies.
In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.
Recent work in unsupervised feature learning and deep learning has shown that be-ing able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network train-ing. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k cate-gories. We show that these same techniques dramatically accelerate the training of a more modestly-sized deep network for a commercial speech recognition ser-vice. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm.
Purpose
While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making.
Design/methodology/approach
The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings.
Findings
Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations.
Originality/value
The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.
Manufacturing incumbents find it difficult to integrate AI in their traditional business models. This paper draws on the research question: How does manufacturing incumbents use AI for enabling business model innovation in industrial ecosystems? We use qualitative method in order to study four large global manufacturing incumbents that are transforming their business models with AI. We performed more than 30 semi-structured in-depth interviews with strategic key personnel in order to understand how they have succeeded with implementing AI and transforming business models. Our main contribution establishes the need for AI business-model innovation to be aligned with ecosystem innovation. Specifically, in short-term incumbents may use ecosystem reconfiguration strategy, whereas long-term strategies relate to ecosystem revitalization, and resilience. Thus, we contribute by connecting organizational microelements with ecosystem macro dimensions and provide an evolutionary model envisioning how incumbents attempt to promote strategic transitions in their firms and ecosystems.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Looking for ways to handle the transition to a digital economy Robots, artificial intelligence, and driverless cars are no longer things of the distant future. They are with us today and will become increasingly common in coming years, along with virtual reality and digital personal assistants. As these tools advance deeper into everyday use, they raise the question-how will they transform society, the economy, and politics? If companies need fewer workers due to automation and robotics, what happens to those who once held those jobs and don't have the skills for new jobs? And since many social benefits are delivered through jobs, how are people outside the workforce for a lengthy period of time going to earn a living and get health care and social benefits? Looking past today's headlines, political scientist and cultural observer Darrell M. West argues that society needs to rethink the concept of jobs, reconfigure the social contract, move toward a system of lifetime learning, and develop a new kind of politics that can deal with economic dislocations. With the U.S. governance system in shambles because of political polarization and hyper-partisanship, dealing creatively with the transition to a fully digital economy will vex political leaders and complicate the adoption of remedies that could ease the transition pain. It is imperative that we make major adjustments in how we think about work and the social contract in order to prevent society from spiraling out of control. This book presents a number of proposals to help people deal with the transition from an industrial to a digital economy. We must broaden the concept of employment to include volunteering and parenting and pay greater attention to the opportunities for leisure time. New forms of identity will be possible when the "job" no longer defines people's sense of personal meaning, and they engage in a broader range of activities. Workers will need help throughout their lifetimes to acquire new skills and develop new job capabilities. Political reforms will be necessary to reduce polarization and restore civility so there can be open and healthy debate about where responsibility lies for economic well-being. This book is an important contribution to a discussion about tomorrow-one that needs to take place today.
This study sought to identify the determinants of chief officers of newspapers in introducing robot journalism into newsrooms. Analytic hierarchy process was employed as a methodology, and data to be analysed were obtained from 42 surveys on chief officers from 24 different newspaper companies. According to the results, a prospected business performance brought about by the introduction of robot journalism and news consumers' willingness to read robot-written news stories are top concerns among the criteria for the consideration of whether newspaper companies plan to introduce robot journalism or not. On the other hand, journalists' attitude towards robot journalism is behind considerations for business performance and changes of external market environment. Decision-makers in newspaper companies seemed to be insensitive to sunken costs with regard to the introduction of robot journalism. In terms of alternatives, a decrease in the number of human journalists after the adoption of robot-writers is most likely to be selected as an employment strategy by newspaper companies. The fall in the number of human journalists after adopting robots is most likely to be selected as an employment strategy.
Whenever a business enterprise is established, it either explicitly or implicitly employs a particular business model that describes the design or architecture of the value creation, delivery, and capture mechanisms it employs. The essence of a business model is in defining the manner by which the enterprise delivers value to customers, entices customers to pay for value, and converts those payments to profit. It thus reflects management's hypothesis about what customers want, how they want it, and how the enterprise can organize to best meet those needs, get paid for doing so, and make a profit. The purpose of this article is to understand the significance of business models and explore their connections with business strategy, innovation management, and economic theory.
Information technology (IT) outsourcing has been one of the critical issues in organization management. Business process outsourcing (BPO) is an advanced type of IT outsourcing, which will be the next big wave in information technology services. Nevertheless, since prior studies have only examined issues related to traditional IS outsourcing, there have been few researches that introduce and discuss the important factors that should be considered in the decision of BPO adoption. This paper identifies factors affecting the BPO decision and structures a decision model using the analytic hierarchy process (AHP) method.By proposing a quantitative decision model, this paper will not only assist management in making better decisions in outsourcing their business processes, but also help them to obtain the most benefits from BPO.
Purpose
To innovate the company business model, executives must first understand what it is, and then examine what paths exist for them to improve on it. This article aims to examine this issue.
Design/methodology/approach
The article provides a practical definition of business models and offers a Business Model Framework (BMF) that illuminates the opportunities for business model innovation.
Findings
The article finds that BMF sequences possible business models from very basic (and not very valuable) models to far more advanced (and very valuable) models. Using the BMF, companies can assess where their current business model stands in relation to its potential and then define appropriate next steps for the further advancement of it.
Practical implications
An organization must give a senior manager the resources and authority to define and launch business‐model experiments.
Originality/value
The article provides a cogent model for assessing the potential for new business model innovation, a framework for carrying it out and a management plan for decision making.
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