Table 1 - uploaded by Craig S. Fleisher
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Four-level hierarchical model of analysis failures.
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It is anticipated that any Competitive Intelligence (CI) professional would want to perform the analysis task and execute their responsibilities successfully over time. Such competencies would normally come with added experience on the job, as should the ability to reduce the risk of failure by diagnosing potential pitfalls. This paper presents: (a...
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Context 1
... hierarchical model of analysis failure Whatever the reasons experienced for CI analysis failures, it is valuable to identify why it happens and this is represented in a four-level model for identifying the barriers to generating effective CI analysis. These four levels, and the primary factors associated with each, are illustrated in Table 1. ...
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Citations
... The originating factors of these pitfalls were identified by McGonagle (2007), , Tropotei (2018) as being the breadth and complexity of intelligence needs, clear articulation by the decision-maker, completion and sequential nature of the cycle, reliance on CI practitioners to run it, capability to deliver timely insights given the environment exponential speed of change, as previously mentioned in the introduction section and referred here for convenience. The pitfalls themselves, as well as their application to individual phases, have long been identified by Fleisher & Wright (2010) and Fleisher & Bensoussan (2015) as being problem definition, project planning, datagathering error, tool-and -technique error, synthesis error, communication transmission or channel error, communication reception error, and unsystematic development. Fleisher & Bensoussan (2015) further highlights that the Intelligence Cycle also struggles with the fact of knowing when to stop one phase and start the next, or another within the cycle, increasing the time to get to the insight and compromising the timely response to intelligence needs. ...
The Competitive Intelligence (CI) construct must be scientifically
defined, characterised, empirically validated, and accurately measured to
grow in science and business. This study aims at elevating the accuracy
of the empirical validation of the CI construct suggested and confirmed
by Madureira, Popovic, & Castelli1,2 to serve as the scientific foundation
for CI praxis. This construct is selected due to its unmatched recency,
thoroughness, and universality and identified limitations of its empirical
validation. We relied on a multistrand design of fully sequential with
equivalent status qualitative and quantitative mix-methods followed by
the triangulation of the findings and the development of the metainferences.
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computer-aided text analysis and artificial intelligence methods based on
61 in-depth interviews with CI subject matter experts. Contributions to
knowledge advancement and relevance to practice derive from the
scientific grade empirical construct validation, providing undisputed
levels of content, discriminant, external accuracy, reliability and
triangulation of results. This study highlights three critical implications.
First, the delimitations of the body of knowledge and recognition of the
CI domain serve as the baseline for theory development. Second, the
validated construct guarantees reproducibility, replicability and
generalisability, laying the foundations for establishing a CI science,
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... The originating factors of these pitfalls were identified by and McGonagle (2007), Calof, Richards, et al. (2017), Tropotei (2018) as being the breadth and complexity of intelligence needs, clear articulation by the decision-maker, completion and sequential nature of the cycle, reliance on CI practitioners to run it, capability to deliver timely insights given the environment exponential speed of change, as previously mentioned in the introduction section and referred here for convenience. The pitfalls themselves, as well as their application to individual phases, have long been identified by Fleisher & Wright (2010) and Fleisher & Bensoussan (2015) as being problem definition, project planning, datagathering error, tool-and -technique error, synthesis error, communication transmission or channel error, communication reception error, and unsystematic development. Fleisher & Bensoussan (2015) further highlights that the Intelligence Cycle also struggles with the fact of knowing when to stop one phase and start the next, or another within the cycle, increasing the time to get to the insight and compromising the timely response to intelligence needs. ...
This research focuses on Competitive Intelligence (CI) as a precursor of sound decisions that improve and sustain the performance of organisations in an exponentially changing world. The absence of an empirically validated scientific definition is a foundational gap that hinders its art, practice and science. So far, the lack of conceptual consensus and delimitation of the body of knowledge inhibited the establishment of a profession, education, and the advancement of the discipline. Thus, the aim of this study is to lay down the foundations of CI science and the developments of its praxis. A mixed-methods approach was used to derive a scientific definition and provide an empirical validation grounded in the expertise of top subject matter experts. A meta-analysis supported the development of a unified, integrative, thorough maturity model, and a set of instruments to guide its implementation and development. Furthermore, the design thinking mindset was identified as appropriate in support of its practice. Overall, the empirically validate scientific definition, the maturity model, the implementation frameworks, and the design thinking mindset are expected to put the CI virtuous cycle in motion catapulting its praxis into a new era. More importantly, they should contribute to improving the performance of professionals, organizations, industries and countries, differentiating CI versus related disciplines, promoting admittance in academe, and mobilising all stakeholders towards the development of its practice, education, and theoretical advancement.
... The research further advanced to how firms construct meaning from the collected information and how the weak signals can be used as a basis for decision making (Daft and Weick, 1984;Chermack et al., 2001;Chermack, 2004;Nutt, 2007). Disruptive changes in businesses (Bennet and Lemoine, 2014;Abidi and Joshi, 2015;, Chauhan et al., 2021, being beyond the comprehension of accepted historical trends and other forms of forecasting (Fleisher and Wright, 2010), needed added foresight and, thus, was a progressive addition to the armoury of business strategies. ...
Purpose
The purpose of this study is to explore the principal research query and whether strategic foresight deployed by entrepreneurial firms in energy transition aims at crafting future readiness.
Design/methodology/approach
With a focus on entrepreneurial firms working in the alternative energy segment in the Indian context, the intent is to examine the deployment of strategic foresight by incumbent firms and their entrepreneurial journey. The authors have adopted the approach of Eisenhardt for this research. The area of interest for the authors entailing strategic foresight by entrepreneurial firms in energy transition aligns with defining features reflecting the aim of Eisenhardt method for this exploratory research coupled with constructivism.
Findings
While the future scenarios in the energy sector have to be necessarily multiple, their alignment with different geographic, economic, demographic and political outlooks shall be defined by the pathways niched through the deployment of strategic foresight for arriving at those scenarios.
Research limitations/implications
Strategic foresight deployed by entrepreneurial firms has the potential to create future readiness through self-reliant sustainable economic value chains for local populace, thus propagating holistic development in remote regions.
Originality/value
This paper attempts to knit together the domains of strategic foresight, entrepreneurial firms and energy transition through case research and present the future thinking deployed for navigation in uncharted pathways by capturing the foresight component of these incumbent firms chosen through careful case selection. The narrative has been strengthened by the varied interviews across participants and the observations made by the authors during the research work.
... For analytical clarity, business failure refers to a situation where the firm is no longer able to operate as a sustainable entity and is forced to cease operations and lay off any employees (Fleisher and Wright, 2010). This not only prompts the retreat and exit from domestic markets but also foreign ones. ...
Purpose
To the best of the authors’ knowledge, this paper is a first attempt to deal with the phenomenon of new venture failure from the business network perspective of the Industrial Marketing and Purchasing (IMP) Group. In particular, this study aims to explore the post-failure phase of a new venture to investigate what happens to the new venture’s resources and relationships in the aftermath of its failure and the role of the entrepreneur in this process.
Design/methodology/approach
The paper builds on an explorative multiple case study of two failed new ventures, unfolding the failure and post-failure phase: evidence from both cases is confronted and discussed.
Findings
This study shows that the post-failure is a complex phase of recombination of activities and residual resources that may lead to new business opportunities. It is discussed that residual resources influence the direction and extent of post-failure activities in terms of restrictions as well as opportunities to restart new projects or ventures. It is also shown how the entrepreneur deals with the “business remains”.
Originality/value
While much attention has been devoted to new ventures’ failure, the paper focuses on the post-failure phase, an almost neglected topic in industrial marketing research. This study sheds some new light upon the journey through which entrepreneurs come to develop the set of resources, activities and relationships that are not only key to the establishment of the venture but also relevant in the complex and intricate trajectories of post-failure.
... For analytical clarity, business failure refers to a situation where the business is no longer able to operate as a sustainable entity and is thus forced to cease operations and lay off any employees (Fleisher & Wright, 2010;Sheppard, 1994). This not only prompts the retreat and exit from domestic markets, but also foreign ones. ...
... Much of the literature tends to mention the challenges of coping with events. Broadly speaking, scholars have tended to adopt either the deterministic or voluntaristic perspectives to account for business failure (Heracleous & Werres, 2016;Mellahi & Wilkinson, 2004, 2010. The deterministic (environmental factors) perspective attributes business failure to uncontrollable or external factors over which managers have little or no control (see also Mellahi & Wilkinson, 2004). ...
In light of growing scholarly works on business failure, across the social science domains, it is surprising that past studies have largely overlooked how extreme environmental shocks and ‘black swan’ events such as those caused by the coronavirus (COVID-19) pandemic and other global crises, can precipitate business failures. Drawing insights from the current literature on business failure and the unfolding event of COVID-19, we highlight the paradoxes posed by novel exogenous shocks (that is, shocks that transcend past experiences) and the implications for SMEs. The pandemic has accelerated the reconfiguration of the relationship between states and markets, increasing the divide between those with political connections and those without, and it may pose new legitimacy challenges for some players even as others seem less concerned by such matters, whilst experiential knowledge resources may be both an advantage and a burden.
... The planning and direction phase defines the decision makers' intelligence requirements (Pellissier & Nenzhelele, 2013). (Fleisher & Wright, 2010). The CI specialists or analysts use these KITs, which are identified and organized according to three functional categories or types, in order to plan and direct the collection and analysis process (Fatti & Du Toit, 2013). ...
... The CI domain equally posits the use of knowledge management systems to sort, store and disseminate validated intelligence, which should be easily accessible to different levels of intelligence users such as lower, middle and top management (Fleisher, 2008;Teo & Choo, 2001). Technology availability and use within the CI domain is also a necessity for this phase to contribute to the following phase of information analysis (Fleisher & Wright, 2010). ...
... Within the CI domain, the analysis is mainly performed by CI professionals or analysts who are trained to display specific analysis skills. Such skills include: research skills; analytical abilities; an ability to apply qualitative and quantitative methods; the ability to select best methods; as well as the ability to communicate results (Fleisher & Wright, 2010;Tej Adidam et al., 2012). In addition, the CI analysis skills include familiarity and the ability to make use of different tools and methods of analysis, that are industry-specific and complement the organization (Fleisher, 2008;Salvetat & Laarraf, 2013). ...
Sport performance analysts and coaches collect and convert competitor and environmental information for strategic decision making. However, existing studies do not provide a systematic approach for this managerial process. Within the business domain, competitive intelligence (CI) is a proven systematic process, which is applied to enhance organizations’ competitiveness through the systematic collection, conversion and countering of information. Therefore, the objective of this comparative systematic literature review (SLR) was to establish similarities between the business CI elements and sport performance analysis (PA). The review was conducted from the functionalist quadrant, to provide rational explanations where mechanical analogies from the business CI domain were used for modelling and understanding the particular phenomenon of the CI process in sport. A systematic review protocol was established, and a total of 61 articles from the CI (34) and PA (27) domains were included. The SLR revealed substantial similarities of the current sport PA elements to the business CI process. The similarities were used to develop an information management process, which is illustrated as a literature CI model for sport coaches. Specific differences between the two domains indicated that empirical research is needed to fill the gaps of sport CI elements description.
... This is essential in CI process planning. Identified KITs direct the collection and analysis process (Fleisher & Wright, 2010) ...
Sport coaches and business managers follow similar activities to collect, analyse and communicate data related to the opposition, known as competitive intelligence (CI), to gain a competitive advantage through strategic decision making. Little is known about the CI process that coaches follow. This study aimed to develop a CI survey for South African cricket coaches and to determine content and face validity of the items. The CI survey was developed using a mixed-methods approach that integrated at four points during a five-step process. Semi-structured interviews were conducted with high-level cricket coaches and support staff, followed by a constant comparative method of qualitative data analysis with Atlas.Ti TM software. Theory and data-driven interview codes and categories were quantitised. A cluster analysis concatenated the codes into five conceptual themes, each with sub-categories. These themes were used to create scales of the survey, whereas the categories were used to create items. Experts confirmed the content, face and preliminary factorial validity of newly developed scales and items of the CI survey. The newly developed CI survey for cricket coaches is unique within the sporting fraternity and holds promise for researchers to further explore this phenomenon within the social sciences field of enquiry.
... The originating factors of these pitfalls were identified by (McGonagle, 2007;Calof et al., 2017;Tropotei, 2018) as being the breadth and complexity of intelligence needs, clear articulation by the decision-maker, completion and sequential nature of the cycle, reliance on CI practitioners to run it, capability to deliver timely insights given the environment exponential speed of change, as previously mentioned in the introduction section and referred here for convenience. The pitfalls themselves, as well their application to individual phase, have long been identified by (Fleisher & Wright, 2010;Fleisher & Bensoussan, 2015) as being problem definition, project planning, datagathering error, tool-and -technique error, synthesis error, communication transmission or channel error, communication reception error, and unsystematic development. (Fleisher & Bensoussan, 2015) further highlight that the Intelligence Cycle also struggles with the fact of knowing when to stop one phase and start the next, or another within the cycle, increase the time to get to the insight and compromising the timely response to intelligence needs. ...
Competitive Intelligence (CI) is becoming of essence due to the need for improving firm performance in an increasingly volatile, uncertain, complex and ambiguous (V.U.C.A.) world. The CI model, however, has not evolved to address evolving intelligence needs, highlighting an opportunity for further research on how to fit for purpose the CI process itself. This study found that Design Thinking (DT) mindset and process has potential for the application to the CI model, improving efficiency both on the overall process, at each stage and in CI. This paper focus on researching the CI process and recognizing its main pitfalls, explaining how DT can help fix or improve on these, and propose a new process which incorporates the aforementioned results. The final part of the study analyses the implications for both CI practitioners and the CI discipline, while pointing to future research with the aim of validating this suggested framework.
... Consequentemente, o processo de análise de informações pode ser associado a conceitos que envolvam modelos estatísticos e algoritmos de dados promovidos pelo Big Data que beneficia a organização no aspecto de criar cenários do futuro que permitem trazer ainda mais qualidade nos diagnósticos analíticos. O Big Data, assim, permite reduzir o potencial de risco, oferecendo vantagens como redução de custos, assim como, colaborando com diferentes tipos de análises que permite criar uma reação rápida de forma a contribuir com a mitigação de riscos (Fleisher & Wright, 2010). ...
Neste início do século XXI, época de geração de grandes volumes de dados, surge uma necessidade emergente para as organizações buscarem novas formas de processar e analisar os dados em benefícios dos negócios. Neste contexto, esta revisão sistemática da literatura oportuniza na descoberta de possibilidades baseada na união de dois importantes conceitos que auxiliam em processos analíticos de dados: o Big Data na perspectiva de uso de tecnologias para viabilizar a qualidade de dados e a Inteligência Competitiva que se refere a um conjunto de ações inteligentes que potencializam a capacidade competitiva da organização. Dessa forma, o estudo desenvolve uma análise bibliométrica descrevendo possibilidades de contribuições baseada em artigos que descrevem alguma relação de união destes dois conceitos, colaborando no reconhecimento de características que serão úteis nas tomadas de decisões, inovação e outras competências que possibilitaram melhores desempenhos para as organizações nesta Era de Grande Volume de Dados. Abstract: At the beginning of the 21st century, the era of generating large volumes of data, an emerging need arises for organizations to seek new ways of processing and analyzing data in business benefits. In this context, this systematic review of the literature allows the discovery of possibilities based on the union of two important concepts that help in data analytical processes: Big Data in the perspective of using technologies to enable data quality and Competitive Intelligence that refers to a set of intelligent actions that enhance the competitive capacity of the organization. In this way, the study develops a bibliometric analysis describing possibilities of contributions based on articles that describe some relation of union of these two concepts, collaborating in the recognition of characteristics that will be useful in decision-making, innovation and other competences that allow better performances for the organizations in this Age of Large Volume Data.
... Moreover, panel B shows that trademarks can significantly increase the survival rates of firms. Fleisher and Sheila (2010) demonstrate that branding and quality certifications are signals to the market that the firm will supply products with the promised quality. Furthermore, they provide a barrier to effective competition from other competitors and increase specialisation in production. ...
The impact of innovation mechanisms on firm survival remains inconclusive in the existing literature, especially when we consider the case of a rapidly growing emerging economy. Using a unique firm-level dataset of 14,065 Chinese high-tech start-ups from 2007 to 2013, we employ a discrete time hazard model to study the impact of differences in internal and external innovation mechanisms, specifically, innovation efficiency and spillover effect derived from trade, on the likelihood of firms' survival as these factors are currently less understood. Bigger and older technology-intensive firms tend to have lower probalility of exit. Our results suggest that innovation as measured by patents, innovation efficiency and firms' import and export activities can increase the survival rate of Chinese high-tech firms. This implies that policy makers should focus on promoting both internal and external innovation mechanisms to improve the survival of indigenous high-tech firms.
