Academic literature on the topic 'Organizational capabilities, digital analytics, data-rich environments'

In the age of Industry 5.0 (I 5.0), manufacturing SMEs face the critical challenge of balancing technological advancement with environmental sustainability. This study explores the intricate relationships between Industry 5.0 technologies—specifically Big Data Analytics and Artificial Intelligence (BDA-AI), Human-Robot Collaboration (HRC), and the Internet of Things (IoT), and sustainable practices in manufacturing SMEs. Drawing on data from 466 SMEs across key industrial regions, we employ structural equation modeling to uncover the transformative roles of Eco-Innovation. Our findings reveal that Eco-Innovation (Eco-Inn) acts as a pivotal mediator, translating technological capabilities into enhanced environmental performance (EP). This study breaks new ground by examining the ethical implications of Human-Robot Collaboration within Industry 5.0, offering insights into its long-term sustainability impact. By integrating the resource-based view theory, this research presents a novel framework for sustainable manufacturing SMEs in the digital era.

Today, data is one of the most important company resources after human resources. It is therefore important to handle data in an adequate way. More and more organizations choose to advance their data strategies beyond simple data warehousing, so they can analyze bigger and bigger data that is produced in a more real-time basis, in a distributed manner, and with increasing variety. Over the past decade, different technologies have emerged which promise to be the answer to the big and fast data challenge. Organizations utilize these technologies in different ways, as well as different combinations (Dean & Ghemawat, 2008; Chauhan & Saxena, 2022; Arora & Talwar, 2023). Traditional techniques that move data to a Data Warehouse to transform it with a set of users’ business rules and create a DW model for easy business reporting have proven not only to be slow, but also not to have the capabilities needed to support advanced data analysis. Organizations have been attempting to augment their DW or replace it altogether with best practices that take advantage of flexible techniques. In doing this, they have learned that while the front-end report access process is a critical part of a data architecture, it is not the only component. In addition to the DW (often augmented with big data capabilities), organizations have created separate and unique big data environments to support custom application development, business intelligence, and advanced analytics. Data from both environments are often served to business reports, for true enterprise reporting. Both types of environments have different requirements and play different roles, and we believe that the solutions to the new data challenges require a new data architecture that takes advantage of the strength of both types of environments in combination, each with different technologies tailored to their respective strengths and weaknesses (Elgendy & Elragal, 2021; Malik, 2023).

The evolving landscape of the 21st century is witnessing a convergence of technological advancements and social science, leading to a paradigm shift in the realm of job satisfaction. This abstract delves into the intricate interplay between these elements, exploring how futurist trends are reshaping the way individuals perceive and experience satisfaction within their work environments. In the contemporary world, technology serves as the catalyst for transformative shifts in various spheres of life. As automation, artificial intelligence, and data analytics redefine industries, they simultaneously impact the dynamics of job satisfaction. Automation has redefined traditional roles by relegating repetitive tasks to machines, thereby liberating human potential for more creative and innovative endeavours. This shift towards more fulfilling work experiences inherently contributes to an elevation in job satisfaction levels. The emergence of remote work and flexible arrangements represents another transformative facet of technological advancement. Enabled by advanced communication tools and connectivity, individuals can engage in meaningful work while transcending geographical constraints. This trend not only augments work-life balance but also bestows a sense of autonomy and empowerment, elevating job satisfaction. Data analytics, as a cornerstone of the technological age, is fundamentally altering decision-making processes within organizations. Employees are no longer passive recipients of directives but active contributors, shaping strategies through data-driven insights. This participatory approach fosters a deeper sense of ownership and engagement, positively impacting job satisfaction. In tandem with the rise of technology, continuous learning and upskilling have become paramount. The dynamic nature of the modern workforce demands adaptability and a commitment to lifelong learning. As individuals embrace the opportunities for skill enhancement, job satisfaction is derived from personal growth and professional development. Customization and personalization are defining facets of the technological age. With the aid of technology, job roles can be tailored to align with an individual's strengths and aspirations. This results in a more harmonious fit between the nature of work and an individual's innate capabilities, nurturing a heightened sense of job satisfaction. Despite the myriad advantages brought about by technological advancements, certain challenges must be navigated. The pervasive nature of technology can inadvertently lead to digital fatigue and social isolation, impacting overall well-being and job satisfaction. Additionally, concerns about data privacy and security in a technology-driven environment necessitate careful ethical considerations. In conclusion, the paradigm shift in job satisfaction under the influence of futurist trends and technological advancements presents a tapestry of empowerment, flexibility, and personalization. As organizations and individuals navigate this transformation, preserving the human aspect of work while harnessing technology's potential becomes paramount. The journey ahead involves striking a delicate equilibrium between innovation and the innate human need for meaningful, satisfying work experiences.

This research explores the transformation of negotiation strategies driven by the rapid advancement and integration of artificial intelligence (AI). In an era of digital acceleration, traditional negotiation practices, primarily relying on human intuition, experience, and emotional intelligence, are increasingly supplemented by AI-driven automated processes. The main objective of this research is to examine how artificial intelligence reshapes established negotiation frameworks critically, impacts decision-making efficiency, and contributes to strategy optimization across various sectors, including business, diplomacy, and international relations. The research methodology includes a comprehensive approach combining historical-logical analysis to track the evolution of negotiation theory, comparative analysis to contrast traditional and AI-enhanced negotiation models, detailed case studies for empirical evidence, and predictive modelling to forecast future trends and scenarios. Historical-logical analysis allows for examining foundational theories such as game theory and the "win-win" negotiation principle, providing context and depth to contemporary practices. Comparative analysis highlights key differences and advantages of AI-driven approaches over traditional methods, while case studies from global corporations offer practical insights into real-world applications and their outcomes. Predictive modelling helps identify future opportunities, risks, and potential scenarios of AI integration into negotiation processes. Artificial intelligence technologies have revolutionized numerous aspects of human activity, marking a significant shift towards algorithm-based decision-making. AI integration into negotiation processes allows for the automation of routine tasks, significantly enhancing the efficiency of data analysis, scenario modelling, and outcome prediction. This technological shift is illustrated through practical examples from leading global corporations such as Amazon, Netflix, JPMorgan Chase, and Walmart. Each case study demonstrates tangible economic benefits from AI applications, including improved supply chain management, optimized content acquisition strategies, and streamlined financial operations. However, introducing AI into negotiation practices alongside these substantial advantages presents new ethical, legal, organizational, and socio-economic challenges. Ethical considerations are paramount, particularly regarding transparency and accountability of AI systems. The research underscores the necessity of maintaining human oversight to mitigate potential biases and ensure fairness, especially in sensitive domains such as recruitment, legal negotiations, and diplomatic interactions. A key outcome of the analysis is the identification and exploration of hybrid negotiation models, which effectively integrate human cognitive capabilities (creativity, empathy, and intuition) with the analytical power of AI (precision, scalability, and speed). Such hybrid models are proposed as optimal solutions that leverage the strengths of both human negotiators and AI systems to achieve superior negotiation outcomes. This research anticipates future trends, suggesting that negotiation strategies increasingly depend on sophisticated algorithmic support. Furthermore, it predicts that negotiation practices will evolve significantly over the next decade, driven by continuous technological advancements such as blockchain for enhanced transparency, quantum computing for faster scenario analysis, and the integration of virtual environments like the metaverse for expanded negotiation contexts. In conclusion, this research advocates for a balanced approach to AI integration, emphasizing the importance of developing clear international standards and ethical guidelines to navigate the complex landscape of automated negotiations. By fostering a comprehensive understanding of opportunities and risks, organizations can successfully adapt to the evolving negotiation environment, leveraging AI to enhance strategic decision-making while preserving fundamental human values.

Abstract Asset management, a process that encompasses recording, data collection, reporting, and documentation of assets, is crucial for supporting operational activities, in maintenance, repair, and procurement. It is a systematic initiative within an organization aimed at identifying, monitoring (cataloguing), categorizing, and attributing ownership to organizational assets throughout their lifespan. The primary goal is to safeguard these assets, preventing the introduction of unforeseen risks. These assets encompass various elements such as technology and other business-related hardware, ph

In the context of a digital economy marked by constant technological advancements, including artificial intelligence, big data, cloud computing, and the Internet of Things, organizations are evolving into interconnected entities. This interdependence necessitates simultaneous changes across all departments, particularly human resources, which serves as the catalyst for modern organizations. Traditional HR approaches are increasingly inadequate in meeting the demands of this dynamic environment, failing to efficiently manage talent identification, selection, development, retention, and overall

Abstract Industrial businesses face increased pressure to drive operational excellence, and that includes keeping people safe and managing costs. Legal compliance is a requirement, and budget constraints are a reality. This paper examines how connected technology can help streamline safety processes and improve worksite efficiency. We discuss how data and real-time analytics from digitally connected devices using the Industrial Internet of Things (IIOT) can improve organizational productivity, compliance and safety performance including, most crucially, response time to life-threatening incide

Abstract Oil & gas industry player have always been big investors in advancement of technology, especially in the direction of extracting additional petroleum to address the production decline. In the spirit of automation, PETRONAS has various automated technical workflows that tackles different types of challenges and purposes. The operational, technical and engineering aspects of increasing production and effectiveness of execution are built upon these processes related to automation of data sources as well as systems integration. With the recent challenge that forced the employees to wo

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective

Abstract Collaborative Working Environments (CWE) are a business solution that improve the quality and speed of decision making by enriching the collaboration between teams and individuals, which results in tangible business benefits. The advantages of working in a collaborative environment are well understood in the organization and the concept is widely embraced throughout the petroleum industry. CWEs provide seamless communication between disciplines and between teams in different locations. Traditionally, they have been used to connect staff in remote locations to teams in the headquarters

To protect human health and natural ecosystems, wastewater treatment plants (WWTPs) have been traditionally designed to remove pollutants from wastewater. With remarkable success WWTPs have adapted to increasingly stringent discharge limits over the years. Nowadays, municipal wastewater treatment facilities are facing a double transition. On the one hand, the transition towards sustainability and the circular water economy, in which resource recovery from wastewater (water recovery, energy recovery and nutrient recovery) plays a fundamental role for its effective implementation. Note that the

Aim/Purpose: The study aims to elucidate the intricate relationship between Organizational Learning (OL) and Knowledge Management (KM), two pivotal organizational capabilities whose interplay remains enigmatic. This study ventures into the depths of this relationship, seeking to demystify the interplay between the structured, strategic management of knowledge and the more fluid, organic process of learning within an organization. Background: Organizational knowledge dynamics pivot on the axis of individual and collective learning processes. KM refers to a multidisciplinary approach to achievin