A systematic literature review of mining weak signals and trends for corporate foresight

Mühlroth, Christian; Grottke, Michael

doi:10.1007/s11573-018-0898-4

Cited by 39 publications

(31 citation statements)

References 109 publications

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“…In order to understand transformation, we first discuss literature on how change processes in the corporate environment have so far been investigated. Companies have to align their strategic decisions and management actions with their environment, which is subject to constant change (Mühlroth and Grottke 2018). These dynamics can provide new growth opportunities for companies (Veugelers et al 2010) but can also pose a fundamental threat (von der Gracht et al 2010).…”

Section: Change Processes and Corporate Adjustmentsmentioning

confidence: 99%

“…Although it is relatively easyier to identify the major change processes, it is much more difficult to translate their exact meaning for a specific industry, the relevant markets, or even an individual company (Cook 2015). The aim of previous research has been to detect changes as early as possible, in order to assist decision makers in identifying possible threats and exploring opportunities (Mühlroth and Grottke 2018). However, these studies do not take into account the very diverse ways in which the change processes affect different players in an industry.…”

Section: Change Processes and Corporate Adjustmentsmentioning

confidence: 99%

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Transformation of the real estate and construction industry: empirical findings from Germany

Pfnür

Wagner

2020

J Bus Econ

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The German real estate and construction industry is facing a transformation, triggered by fundamental changes in technology, the economy and society. The purpose of this paper is to explore how these changes exert pressure to adapt on existing business models, while also offering opportunities for their further development. A model that attributes the transformation of the real estate industry to megatrends, specific structural change drivers, business models and the interaction of the groups of players in the value creation system is developed. The model is tested empirically by a survey. Structural equation modelling is used to measure the impact of various determinants of transformation and the relevance of adaptation strategies to the different groups of players. As a result, the study explains three impact mechanisms of the transformation in the real estate industry. The transformation originates from the occupiers, who need more flexible space and are focusing on holistic solutions that are not provided by the real estate industry. Service providers are striving to increase the efficiency of their traditional processes, rather than evolving new business models or services. The investors recognise the increasing importance of the occupiers but they are rarely pursuing the resulting strategies. Property developers recognise these requirements: their adaptation strategies most strongly question their previous business. For practitioners, the results point out the opportunities and risks of transformation and help to illustrate the need for change. For academicians, the results indicate a way to explain and measure the transformation of a value creation system.

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Section: Change Processes and Corporate Adjustmentsmentioning

confidence: 99%

Section: Change Processes and Corporate Adjustmentsmentioning

confidence: 99%

Transformation of the real estate and construction industry: empirical findings from Germany

Pfnür

Wagner

2020

J Bus Econ

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“…Scholars argue that determining which application to use depends on the type, quantity, and quality of data available, resulting in various necessities to handle the data, such as classification, clustering, or detection of connections (see Table 3). Moreover, as several applications can be used for both top-down and bottom-up approaches (Flath and Stein 2018;Mühlroth and Grottke 2018;Baryannis et al 2019a, b;Blasch et al 2019), the purpose for which the specific application should be used is identified as an additional influence (Blasch et al 2019).…”

Section: Categorization Of Ai Applicationsmentioning

confidence: 99%

“…The final decision, however, must be taken by the human decision group only. With the current state of technology available, AI can thus leverage the stages of input and process (Bohanec et al 2017a;von Krogh 2018), with the most significant impact on knowledge management (Mühlroth and Grottke 2018;Blasch et al 2019). This indicates that most existing applications cannot be defined as AI at all based on Nilsson's (2010) definition of intelligence.…”

Section: Conceptual Framework For Ai Integration Into the Organizatiomentioning

confidence: 99%

On the current state of combining human and artificial intelligence for strategic organizational decision making

Trunk

Birkel

Hartmann

2020

Bus Res

123

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Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI, other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

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“…Paradoxically, the more immersed in data and algorithms we become, the more critical is it that we skillfully deploy carefully chosen algorithms to navigate our information journey. In a comprehensive literature review, Mühlroth and Grottke (2018) analyzed fifty foresight articles that used a combination of expert and computer-driven scanning for weak signals. The authors found that queries that were led by experts were more prone to human bias than those that were fully computer-driven.…”

Section: Environmental Scanning Through Direct Observation In Big Datamentioning

confidence: 99%

Mine the Gap: Augmenting Foresight Methodologies with Data Analytics

Boysen

2020

World Futures Review

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The explosion of Big Data and analytic tools in recent years has brought new opportunities to the field of foresight. Big Data and improved analytics capabilities can expand the knowledge base and act as a corrective to our cognitive biases. Moreover, several data mining and machine learning techniques that increase performance for businesses can be applied in foresight to help researchers discover patterns that may be early signals of change and correct our misperception of patterns where they don’t exist. This article discusses the opportunities and limitations of various data mining and machine learning techniques in foresight.

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A systematic literature review of mining weak signals and trends for corporate foresight

Cited by 39 publications

References 109 publications

Transformation of the real estate and construction industry: empirical findings from Germany

Transformation of the real estate and construction industry: empirical findings from Germany

On the current state of combining human and artificial intelligence for strategic organizational decision making

Mine the Gap: Augmenting Foresight Methodologies with Data Analytics

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