Showing papers on "Competitive advantage published in 2020"

Literature review of Industry 4.0 and related technologies

Abstract: Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Sustainable Business Models: A Review

Abstract: During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

The digital revolution in the travel and tourism industry

Abstract: The digital revolution is radically changing the world we live in. Sensors in smart homes are able to interconnect devices such as thermostats, washing machines, television sets, laptops, tablets, and other objects to the Internet of Things platforms. New digital technologies have introduced important innovations in factories, hospitals, hotels, cities and territories. Industry 4.0 is signaling the end of well-established patterns and is asking scholars, managers and citizens willing to survive in this ever-changing and increasingly complex environment to observe it through different lenses and new paradigms. The tourism sector, also, is very much involved in digital transformations, increasingly qualifying them with expressions such as Tourism 4.0 or Smart Tourism. What impact does the digital revolution have on tourism? What do tourism 4.0 and smart tourism have in common? What are the main differences? Adopting a conceptual approach and focusing on the travel and tourism industry, our work aims to provide a point of view and some preliminary answers to the above questions. In paragraph 1 we illustrate the main changes brought about by the digital revolution 4.0 in industry, the Web, and tourism. Then, the concepts of tourism 4.0 and smart tourism are compared in Sect. 2. Section 3 illustrates how the consumer experience of digital tourists changes before, during, and after the trip. Paragraphs 4 and 5 illustrate the challenges of tourism destinations and tourism ecosystems in the smart perspective. In the final section of the paper, we highlight that in the near future it will not be possible for tourism ecosystems and territories to only take into account digital innovations, but they will have to include smart tourism perspectives like sustainability, circular economy, quality of life, and social value; they should also aim to enhance tourism experiences and to increase the competitive advantage of smart tourism destinations. Tourism 4.0 technologies need to be geared toward the improvement of the quality of tourism practices, assuming smartness and sustainability as the right paradigm for improving the quality of life and the social value of both guests and local residents.

Perceived Greenwashing: The Effects of Green Marketing on Environmental and Product Perceptions

Abstract: Many firms are striving to improve their environmental positions by presenting their environmental efforts to the public. To do so, they are applying green marketing strategies to help gain competitive advantage and appeal to ecologically conscious consumers. However, not all green marketing claims accurately reflect firms’ environmental conduct, and can be viewed as ‘greenwashing’. Greenwashing may not only affect a company’s profitability, but more importantly, result in ethical harm. Therefore, this research extends past greenwashing studies by examining additional influences on and outcomes of perceived greenwashing. To do so, we conducted two studies, an interview study with consumer product and consulting firms, as well as an experiment examining consumers interacting with a company website. For these studies, we used multiple methods, including interviews, questionnaires, and neurophysiological techniques. We found that perceived greenwashing relates not only to environmental and product perceptions, but also to consumers’ happiness while interacting with the website. We also found that website interactivity relates to perceived greenwashing, environmental and product perceptions, and to the amount of interaction with the website. We conclude by discussing managerial and ethical implications for research and practice.

Green product innovation, green dynamic capability, and competitive advantage: Evidence from Chinese manufacturing enterprises

Abstract: This study focuses on how green product innovation affects green dynamic capability and competitive advantage in the Chinese manufacturing industry. The empirical research indicates that green product innovation is positively correlated with both competitive advantage and green dynamic capability, whereas green dynamic capability also has a significant impact on competitive advantage. Furthermore, this paper finds that the resource integration capability, resource reconfiguration capability, and environmental insight capability of green dynamic capability play intermediary roles between green product innovation and competitive advantage. Corporate reputation positively moderates the relationship between green product innovation and resource integration capability and positively moderates the relationship between green product innovation and resource reconfiguration capability, whereas there is no moderating effect between green product innovation and environmental insight capability.

Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects

Abstract: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.,This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.,AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.,AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.,This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

Customer experience and engagement in tourism destinations: the experiential marketing perspective

Abstract: Tourism destinations are increasingly offering experiential services to promote the development of their competitive advantage. This research investigates the effects of customers’ tourism engageme...

Corporate Economic, Environmental, and Social Sustainability Performance Transformation through ESG Disclosure

Abstract: Within the environmental, social, and governance (ESG) disclosure–corporate sustainability performance (economic, environmental and social; EES) framework, our empirical analysis examined the impact of ESG information disclosure on EES sustainability performance among Asian firms from 2005 to 2017. The positive ESG disclosure–EES sustainability performance relationship found in this study provides evidence that disclosing the implementation of environment and social strategies within an effective system of corporate governance in the organization strengthens corporate sustainability performance. The results also show that environmental performance and social performance are significantly positively related to economic sustainable performance, indicating that the corporation’s economic value and creating value for society are interdependent. In line with the stakeholder theory and the shared value theory, ESG information disclosure to all stakeholders is an important factor in creating a competitive advantage for enhancing corporate sustainability performance.

Role of artificial intelligence in operations environment: a review and bibliometric analysis

Abstract: “Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.,As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).,Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.,The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Innovation in the Era of IoT and Industry 5.0: Absolute Innovation Management (AIM) Framework

Abstract: In the modern business environment, characterized by rapid technological advancements and globalization, abetted by IoT and Industry 5.0 phenomenon, innovation is indispensable for competitive advantage and economic growth. However, many organizations are facing problems in its true implementation due to the absence of a practical innovation management framework, which has made the implementation of the concept elusive instead of persuasive. The present study has proposed a new innovation management framework labeled as “Absolute Innovation Management (AIM)” to make innovation more understandable, implementable, and part of the organization’s everyday routine by synergizing the innovation ecosystem, design thinking, and corporate strategy to achieve competitive advantage and economic growth. The current study used an integrative literature review methodology to develop the “Absolute Innovation Management” framework. The absolute innovation management framework links the innovation ecosystem with the corporate strategy of the firm by adopting innovation management as a strategy through design thinking. Thus, making innovation more user/human-centered that is desirable by the customer, viable for business and technically feasible, creating both entrepreneurial and customer value, and boosting corporate venturing and corporate entrepreneurship to achieve competitive advantage and economic growth while addressing the needs of IoT and Industry 5.0 era. In sum, it synergizes innovation, design thinking, and strategy to make businesses future-ready for IoT and industry 5.0 revolution. The present study is significant, as it not only make considerable contributions to the existing literature on innovation management by developing a new framework but also makes the concept more practical, implementable and part of an organization’s everyday routine.