Modeling and prediction of business success: a survey

Artificial Intelligence Review, Год журнала: 2024, Номер 57(2)

Опубликована: Фев. 9, 2024

Abstract Businesses are the driving force behind economic systems and lifeblood of community. A business shares striking similarity to a living organism, including birth, infancy, rising, prosperity, falling. The success is not only important owners, but also critical regional/domestic system, or even global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc., yet millions fail fade out within rather short period time. Finding patterns/factors connected rise fall remains long lasting question puzzling economists, entrepreneurs, government officials. advancement in artificial intelligence, especially machine learning, has lend researchers powers use data model predict success. However, due driven nature all learning methods, existing approaches domain-driven ad-hoc their design validations. In this paper, we propose systematic review modeling prediction We first outline triangle framework showcase three parities business: Investment-Business-Market (IBM). After that, align features into main categories, each which focused on from particular perspective, sales, management, innovation further summarize different types deep methods for prediction. survey provides comprehensive computational performance

Язык: Английский

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What “V” of the big data influence SMEs' open innovation breadth and depth? An empirical analysis

R and D Management, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

The open innovation (OI) paradigm has garnered relevant attention in recent years. Against this backdrop, study explores the impact of a relatively phenomenon, such as Big Data, terms Volume, Velocity, and Variety, on small medium enterprises' (SMEs') OI search. In fact, while issues related to Data have been often examined context high‐tech firms, effects SMEs' search strategies not extensively studied. This paper addresses gap by developing quantitative analysis sample 123 Italian SMEs. findings reveal that significantly influences breadth, leading increased external collaborations. parallel, they do affect depth. Moreover, varies among different “3Vs” suggesting some characteristics more pronounced effect strategies. Drawing these insights, contributes understanding interplay between OI, offering hopefully valuable implications for both literature proposing avenues further research practice.

Язык: Английский

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Industry 4.0

Advances in logistics, operations, and management science book series, Год журнала: 2024, Номер unknown, С. 342 - 405

Опубликована: Янв. 19, 2024

The advent of Industry 4.0, characterized by the integration digital technologies into industrial processes, has ushered in a transformative era for manufacturing and beyond. This chapter delves future trends research directions that will shape landscape 4.0 coming years. One prominent trend is continued proliferation internet things (IoT) its convergence with artificial intelligence (AI). As IoT devices become more interconnected intelligent, they enable real-time data analysis, predictive maintenance, adaptive manufacturing, fostering increased efficiency cost-effectiveness across industries. Moreover, rise edge computing set to redefine processing analytics. deployment powerful resources closer source promises reduced latency enhanced decision-making capabilities, particularly critical applications like autonomous remote robotics.

Язык: Английский

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Embracing New Disruptions: Business Model Innovation in the Transition to Mobility as a Service (MaaS)

Journal of Cleaner Production, Год журнала: 2024, Номер 464, С. 142744 - 142744

Опубликована: Май 31, 2024

A decade ago, Mobility as a Service (MaaS) has emerged revolutionary concept destined to change the traditional transport paradigm. Indeed, MaaS envisions an integrated and on-demand transportation ecosystem where users have access variety of mobility options via unified digital platform that centralizes information, ticketing, payment systems for different service providers. Recognizing its disruptive potential, in this paper we examinate effects introduction market, with particular focus on businesses within industry. Specifically, aim at investigating dynamic nature business model innovation ecosystem, thus capturing changes, adaptations, innovations organizations undergone, providing valuable insights into new value creation, capture delivery mechanisms emerged. To achieve these goals, employed inductive, multiple case study approach, focusing total six renowned representing two key actor categories ecosystems: providers technology Our findings reveal that, despite their heterogeneity, both adopted similar strategies transition MaaS. Utilizing capture, framework developed distinct models each categories. We identified common firm-specific which been categorized broader macro-level delivery, drawing from existing literature. The contribute ongoing academic literature related innovation. Furthermore, they offer practical managers policymakers, guidance strategic decision-making aimed fostering development sustainability innovative systems.

Язык: Английский

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Big Data Analytics in the Metaverse: Business Value Creation with Artificial Intelligence and Data-Driven Decision Making

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

The metaverse as it is currently envisioned – a persistent and immersive world based on virtual augmented reality an immense business opportunity across all major sectors industries will further increase the volume variety of available data. Those unlimited data streams require modern big analytics artificial intelligence solutions to generate valuable insights. This paper argues that ecosystems generation in those can be harnessed create value through sustained disruptive innovation. Concrete, this explores how development drives innovative industry specific applications use cases companies analytics-driven innovations ecosystems. Further, examines dark side AI solutions, their far-reaching security, ethical, social implications. Finally, concludes with future research agenda.

Язык: Английский

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Artificial intelligence capabilities, dynamic capabilities and organizational creativity: contributing factors to the United Arab Emirates Government’s organizational performance

Journal of Modelling in Management, Год журнала: 2024, Номер 19(3), С. 953 - 979

Опубликована: Янв. 30, 2024

Purpose This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using resource-based theory. It seeks examine impact these on organizational-level resources dynamic and organizational creativity, ultimately influencing overall performance government organizations. Design/methodology/approach The calibration was conducted combination qualitative quantitative analysis tools. A set 26 initial items formed in study. In study, self-reported data obtained from 344 public managers used for purposes refining validating scale. Hypothesis testing is carried out relationship between theoretical constructs purpose nomological testing. Findings Results provide empirical evidence that presence positively significantly impacts capabilities, creativity performance. Dynamic also found partially mediate with performance, mediates – link. Practical implications application holds promise improving decision-making problem-solving processes, thereby increasing perceived value service. can be achieved through implementation regulatory frameworks serve as blueprint enhancing Originality/value There are limited number studies sector, often present conflicting inconclusive findings. Moreover, indicate literature has not adequately explored significance complementarity facilitating development unique within paper presents framework organizations their capabilities-organizational relation, drawing

Язык: Английский

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Can institutional pressures serve as an efficacious catalyst for mitigating corporate carbon emissions?

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(14), С. 21380 - 21398

Опубликована: Фев. 23, 2024

Язык: Английский

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Technology-enabled business model innovation and carbon emission reduction: Evidence from a place-based policy in China

Technovation, Год журнала: 2024, Номер 134, С. 103052 - 103052

Опубликована: Июнь 1, 2024

Язык: Английский

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Digital Economy's influence on R&D Network configurations: Integrating resource dependence theory and institutional theory

Technovation, Год журнала: 2024, Номер 137, С. 103098 - 103098

Опубликована: Сен. 1, 2024

Язык: Английский

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Big data and machine learning-based decision support system to reshape the vaticination of insurance claims

Technological Forecasting and Social Change, Год журнала: 2024, Номер 209, С. 123829 - 123829

Опубликована: Окт. 19, 2024

Язык: Английский

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