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Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests. [ABSTRACT FROM AUTHOR]

There are increasing international efforts to tackle climate change by reducing the emission of greenhouse gases. As such, the use of electrolytic hydrogen as an energy carrier in decentralised and centralised energy systems, and as a secondary energy carrier for a variety of applications, is projected to grow. Required green hydrogen can be obtained via water electrolysis using the surplus of renewable energy during low electricity demand periods. Electrolysis systems with alkaline and polymer electrolyte membrane (PEM) technology are commercially available in different performance classes. The less mature solid oxide electrolysis cell (SOEC) promises higher efficiencies, as well as co-electrolysis and reversibility functions. This work uses a bottom-up approach to develop a viable business model for a SOEC-based venture. The broader electrolysis market is analysed first, including conventional and emerging market segments. A further opportunity analysis ranks these segments in terms of business attractiveness. Subsequently, the current state and structure of the global electrolyser industry are reviewed, and a ten-year outlook is provided. Key industry players are identified and profiled, after which the major industry and competitor trends are summarised. Based on the outcomes of the previous assessments, a favourable business case is generated and used to develop the business model proposal. The main findings suggest that grid services are the most attractive business sector, followed by refineries and power-to-liquid processes. SOEC technology is particularly promising due to its co-electrolysis capabilities within the methanol production process. Consequently, an “engineering firm and operator” business model for a power-to-methanol plant is considered the most viable option.

Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests.

The trend of having a strong digital presence for every organization has become a need. Earlier even though the organizations or associations that did not require a digital presence and could continue working without it, now cannot survive without it all because of the Pandemic Covid 19. This study focuses on such an organization- Calcutta Management Association (CMA) which now needs a strong digital base for it to stand out among its peers and also for its own existence as well. With proper R&D, to come up with strategies, competitor analysis, etc., helping the organization in establishing a better digital footprint. The workings of the competitors/peers are analyzed and compared it with CMA, derived at feasible solutions for it to achieve, stating all the problems that needs to be addressed. The researcher tried to compare the basics through questionnaire sent to 1100 individuals to the existing members of the association and to its peers, to have understanding and familiarity about the members’ expectation and the gap in the activities from other associations. The study carried out from the month of February 2021 to July 2021. Both primary and Secondary research have been involved in the study to reach to decisive state, which would help CMA is rerouting its future course of actions and helps achieve its well-defined objectives.

In the last decade, strategic management accounting (SMA) practices have garnered considerable attention from academics and business organizations. SMA is described as the provision and analysis of management accounting data on a company’s product in the markets, its cost structure, and competitors’ costs, as well as the monitoring of the firm’s and its competitors’ strategic positions in these markets over time. SMA techniques have the potential to provide a wide range of benefits for organizations. These techniques include competitor accounting, customer accounting, strategic costing, strategic planning, control and performance management, and strategic decision making. Despite the high potential of SMA for decision making, there are still issues with practical application and a lack of knowledge about using SMA strategically to achieve business goals. Using a systematic literature review approach, the present study aims to provide a critical literature review to identify the motivation to adopt SMA practices; to identify evidence on the usage of SMA practices; to provide a synthesis of the impacts of SMA on business goals, and to identify the knowledge gaps that exist in the current literature about SMA practices and business goals, highlighting the potential benefits, challenges, and opportunities, and presenting a discussion about future research directions. The review’s main contributions are to provide an in-depth discussion of the peer-reviewed literature in which the term SMA is used, as well as a basis for future research and practice.

There are increasing international efforts to tackle climate change by reducing the emission of greenhouse gases. As such, the use of electrolytic hydrogen as an energy carrier in decentralised and centralised energy systems, and as a secondary energy carrier for a variety of applications, is projected to grow. Required green hydrogen can be obtained via water electrolysis using the surplus of renewable energy during low electricity demand periods. Electrolysis systems with alkaline and polymer electrolyte membrane (PEM) technology are commercially available in different performance classes. The less mature solid oxide electrolysis cell (SOEC) promises higher efficiencies, as well as co-electrolysis and reversibility functions. This work uses a bottom-up approach to develop a viable business model for a SOEC-based venture. The broader electrolysis market is analysed first, including conventional and emerging market segments. A further opportunity analysis ranks these segments in terms of business attractiveness. Subsequently, the current state and structure of the global electrolyser industry are reviewed, and a ten-year outlook is provided. Key industry players are identified and profiled, after which the major industry and competitor trends are summarised. Based on the outcomes of the previous assessments, a favourable business case is generated and used to develop the business model proposal. The main findings suggest that grid services are the most attractive business sector, followed by refineries and power-to-liquid processes. SOEC technology is particularly promising due to its co-electrolysis capabilities within the methanol production process. Consequently, an "engineering firm and operator" business model for a power-to-methanol plant is considered the most viable option. [ABSTRACT FROM AUTHOR]

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers' perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources. [ABSTRACT FROM AUTHOR]

The aim of this paper is to define a method for uncovering smart city technology trends using competitive technical intelligence processes focused on intellectual property analysis using data visualisation. The authors introduce ways of gathering relevant core bibliographic data on the desired technology together with search preparation and query building. For demonstration purposes, the authors have used the European Patent Office datasets. The aim of this paper is to define a method for smart city competitive intelligence analysis focused on the main innovative companies and organizations and specific technology trends. The time period between the years 1997 and 2017 is used. Based on the results, the authors have indicated that Samsung is the leader in intelligent city innovations in this period. Five main product invention categories were also identified.

Monitoring the movements of competitors as well as their innovative endeavors and the targets of their efforts is an important task for the business intelligence of a company. In addition to market-oriented means, such as observing competitors’ publications and websites, or analyzing existing products, patent analysis may offer future-oriented insights. Usually, analysts in companies search for competitors’ patents and analyze them manually, producing qualitative information for the abovementioned task. Apart from this qualitative information, quantitative information might help to measure, visualize, and communicate what competitors are really doing. For this purpose, we develop a method based on semantic anchor points aiming at analyzing the competitive landscape to achieve insights as to how the innovation activities of one company are reflected in the core competencies of another company over time. As novel contributions, we adapt the forming of anchor points for the case of companies and develop several quantitative analysis types, e.g., regarding basic competitor landscaping and high risk analysis. We explain the four steps of our approach and demonstrate its use in monitoring direct competitors in the technology field of gearing, which is part of the automotive industry. Our method can be transferred to other industries due to specific suppositions. It is able to form the core of an automated monitoring system. [ABSTRACT FROM AUTHOR]

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in reviewcomments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Given the competitive mobile app market, developers must be fully aware of users’ needs, satisfy users’ requirements, combat apps of similar functionalities (i.e., competing apps), and thus stay ahead of the competition. While it is easy to track the overall user ratings of competing apps, such information fails to provide actionable insights for developers to improve their apps over the competing apps (AlSubaihin et al., IEEE Trans Softw Eng, 1–1, 2019). Thus, developers still need to read reviews from all their interested competing apps and summarize the advantages and disadvantages of each app. Such a manual process can be tedious and even infeasible with thousands of reviews posted daily. To help developers compare users’ opinions among competing apps on high-level features, such as the main functionalities and the main characteristics of an app, we propose a review analysis approach named FeatCompare. FeatCompare can automatically identify high-level features mentioned in user reviews without any manually annotated resource. Then, FeatCompare creates a comparative table that summarizes users’ opinions for each identified feature across competing apps. FeatCompare features a novel neural network-based model named G lobal-L ocal sensitive F eature E xtractor (GLFE), which extends Attention-based Aspect Extraction (ABAE), a state-of-the-art model for extracting high-level features from reviews. We evaluate the effectiveness of GLFE on 480 manually annotated reviews sampled from five groups of competing apps. Our experiment results show that GLFE achieves a precision of 79%-82% and recall of 74%-77% in identifying the high-level features associated with reviews and outperforms ABAE by 14.7% on average. We also conduct a case study to demonstrate the usage scenarios of FeatCompare. A survey with 107 mobile app developers shows that more than 70% of developers agree that FeatCompare is of great benefit. [ABSTRACT FROM AUTHOR]

Competitor monitoring and tracking is a vital strategic activity for any large company that finds itself in a highly competitive environment. Therefore, the present work aims at perfecting a Competitor Analysis methodology employed by the GGI group (Grupo de Gestão da Informação) of UFSC's Labmat (materials engineering research laboratory). Supported by a bibliographic survey of Competitive Intelligence literature, the first step consisted of determining what were the project's Intelligence Needs, as well as what were the main competitors to be analyzed, which analyses were to be conducted and what tools were to be employed, with the input and assistance of GGI members. The companies that compete in the industrial electric motors industry were chosen as the basis for the case study, from whom publicly available data was collected and analyzed. Based on the conducted analyzes, the competitors were ranked according to their competitive advantage and what were their main strategies employed in order to achieve competitive success. WEG S.A. was chosen as the competitor to be analyzed due to its competitive positioning and also because this choice was the most in line with the project's intelligence needs, reinforcing the importance of following the Intelligence Cycle and conducting macro ambient analysis and industry analysis prior to competitor analysis. It was also possible to reaffirm the applicability of Python programming in the business and strategic spheres and its advantages, as well as the importance of more traditional methodologies and tools, such as PESTEL analysis, FCS analysis and the Competitor Matrix. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate which sources and how competition intelligence (CI) is collected in hospitals, and what are the environmental factors affecting the competitive intelligence activities. And it consists of presenting information on what kind of purposes hospitals use competitive intelligence and a description of how and in what way C.I found a place in the organizational structure in the hospitals that constitute the cases of the study. Multiple case analysis and theoretical sampling logic were used in the study. Through the purpose of the study, according to the data of the health statistics yearbook published by the Ministry of Health in 2017, the Marmara Region, where the population and the number of hospitals are the highest, was selected as the research area. Seven of the hospitals in this region were reached and semi-structured interviews were conducted with eight managers using qualitative research method. The questions were prepared according to the focal points determined to inquire about the hospitals' competitive intelligence activities. The categories developed in this framework and the relationships between these categories were examined with qualitative content analysis. As a result of the study, it was determined that the hospitals that constitute the cases of the study did not have a special competition intelligence unit. It was concluded that the competitive intelligence process took place unofficially and that environmental factors have a significant impact on intelligence practices. In this context, it has been found that competitive intelligence is generally used in tactical decisionmaking activities and that there are sector-specific competition conditions that hospitals should pay attention to in their activities. Another important outcome of the study is that tactical decision-making and environmental features, depending on short-term thinking among the cases, are not yet fully established in the institutional process of the concept of C.I in hospitals. Although there is a certain template in the minds of executives related to C.I, the lack of institutionalization of the concept causes a dilemma regarding C.I among the cases and this dilemma increases the clinging of cases to short-term tactical decisions rather than long-term thinking. [ABSTRACT FROM AUTHOR]

A competitor analysis is a core process in management decision making, and the extraction of competitive factors is a key component in a competitor analysis. Owing to the rapid development of social media, methodologies and frameworks facilitating a competitor analysis through online reviews have recently been proposed. However, existing studies have only focused on the detection of comparative sentences in review comments. Thus, product developers compare the competitive factors only within identical feature dimensions of a product. This study proposes a novel approach to identifying the competitive factors at the comprehensive level of the product features—in other words, the focus is on the points of differentiation of each product as perceived by customers using an explainable neural network. We assume that keywords, which have a significant influence on the classification decision, are considered meaningful points of differentiation by consumers. Thus, we establish a classification model to classify the review comments for each corresponding product and calculate the weight of importance of each keyword in such comments during the classification process. We then extract and prioritize the keywords to determine the competitiveness based on the weight of importance. The experiment results show that our proposed method effectively extracts the competitive factors in both qualitative and quantitative experiments. This is the first study on extracting competitive factors with an explainable neural network based on customer reviews; future studies may extend the scope of the extraction of such factors using an explainable neural network. [ABSTRACT FROM AUTHOR]

The article presents the key indicators reflecting the number of foreign students studying in Ukraine and the volume of demand for educational services in the countries that are their main suppliers. Furthermore, it justifies the procedure of ranking potential competitors of Ukraine in the world market of educational services as well as the selection of components of a comprehensive strategy for the state program to support the export potential of higher education institutions based on the grouping of priority export territories in view of the requirements of potential consumers, who have so far been choosing other countries to study abroad, to the infrastructure of national educational systems. It is proposed to use a combination of ranking methods, cluster and discriminant analysis when choosing priority geographic areas for the export of educational services and a list of measures aimed at improving the international competitiveness of the national education system and individual educational institutions, which together form a universal algorithm for determining the necessary set of measures at successive stages of developing a strategy for the country’s entry into the global market for educational services.

Copyright of International Journal of Engineering & Management Sciences (2498-700X) / Műszaki és Menedzsment Tudományi Közlemények is the property of University of Debrecen and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human capital is a key component of the knowledge economy. Firms compete not only for consumers in the product market but also for human capital in the labor market. In this study, we perform an interfirm labor market competitor analysis using the online profiles of more than 89,000 employees and their career histories that span more than 3,000 public firms. Using these profiles, we characterize firms through the granular skill distribution of their employees. Also, using employee migrations across firms, we derive and analyze a human capital flow (HCF) network. Such information allows us to measure the interfirm human capital overlap in terms of similarity in their employees' skills and HCF network structure. We show that our proposed human capital overlap metrics have superior predictive power over conventional firm-level measures in predicting future labor market competitors. We further demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive two-dimensional competitor analysis that includes both product and labor overlap between firms. By evaluating interfirm relationships in the product and labor markets simultaneously, this two-dimensional competitor analysis framework can help managers make strategic decisions beyond human resources, such as product development and customer relationship management. Human capital is a key component of the knowledge economy that firms compete for in the labor market. Compared with the product market competition, the identification and prediction of labor market competitors have garnered little attention in the literature. In this study, we perform an interfirm labor market competitor analysis with a unique longitudinal employer-employee matched data set derived from online profiles of 89,943 employees, tracking their careers in 3,467 public firms from the years 2000 to 2014. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms. In particular, they allow us to represent firms in the space of the skills possessed by their employees and measure the interfirm human capital overlap in terms of similarity in their employees' skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures. We also demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive competitor analysis that includes both product and labor overlap between firms. [ABSTRACT FROM AUTHOR]

The aim of this paper is to define a method for uncovering smart city technology trends using competitive technical intelligence processes focused on intellectual property analysis using data visualisation. The authors introduce ways of gathering relevant core bibliographic data on the desired technology together with search preparation and query building. For demonstration purposes, the authors have used the European Patent Office datasets. The aim of this paper is to define a method for smart city competitive intelligence analysis focused on the main innovative companies and organizations and specific technology trends. The time period between the years 1997 and 2017 is used. Based on the results, the authors have indicated that Samsung is the leader in intelligent city innovations in this period. Five main product invention categories were also identified. [ABSTRACT FROM AUTHOR]

In this article we propose indices for technological breadth and depth of patent documents. We conceptually compare and contrast several indices that have been used in the existing literature, explaining main limitations. Motivated by the drawbacks of those we demonstrate how Rao's Quadratic Entropy, a statistical index used in ecology for measuring biodiversity, can be decomposed to separately measure technological breadth and depth. The properties of breadth and depth are then investigated using patents for business data processing. For the technological domains with the highest patenting activity, we show how the novel measures of technological breadth and depth can be used to rank the patenting entities by average breadth, and identify ones with inventions that on average exceed the domain-specific average depth. Practical implications of the proposed indices are also exemplified and discussed in the context of competitor analysis for entrepreneurial ventures in the area of network security for business data processing. [ABSTRACT FROM AUTHOR]

Combining the FDI spillover literature with a competitor analysis framework, we examine the relative size of spillover and competition effects in China between foreign entrants and local firms, among foreign entrants, and among local firms. Our results show that the increased presence of foreign entrants has generally benefited local firms nationally, but has negatively affected the survival rates of local firms in regional markets. Surprisingly, foreign entrants are crowded out not only by their peers, but also by reformed local firms at both the national and regional levels. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Competitor identification, a core element of competitive dynamics, has been of long-standing interest to researchers in management and related disciplines. This broad interest has resulted in various definitions and conceptualizations of competitor identification as well as various approaches to studying it, which impairs the integration of existing knowledge aimed at answering vital questions regarding its nature, processes, and implications. To help researchers confront the complexities underlying this phenomenon, we identify, review, and organize theory and research on competitor identification across the management, marketing, and industrial organization economics disciplines. Based on our review, we organize the identified literature into four perspectives on competitor identification labeled as industry-oriented, strategic groups–oriented, manager-oriented, and customer-oriented. For each of these perspectives, we also identify major research streams and these streams' foci and contributions. Building on our review, we propose an agenda for future management research that addresses both unresolved debates in the reviewed literature and identifies new promising connections between competitor identification and specific areas of management theory and research. We organize this future research agenda into six main themes focusing on exploring the dynamics of competitor identification, building connections to research on interorganizational relations, building a practice and process perspective, expanding micro-oriented approaches, and exploring international and entrepreneurial dimensions of competitor identification. [ABSTRACT FROM AUTHOR]