In and Out of Balance: Industry Relatedness, Learning Capabilities and Post‐Acquisition Innovative Performance

Workplace environments have a significant impact on worker performance, health, and well-being. With machine learning capabilities, artificial intelligence (AI) can be developed to automate individualized adjustments to work environments (e.g., lighting, temperature) and to facilitate healthier worker behaviors (e.g., posture). Worker perspectives on incorporating AI into office workspaces are largely unexplored. Thus, the purpose of this study was to explore office workers’ views on including AI in their office workspace. Six focus group interviews with a total of 45 participants were conducted. Interview questions were designed to generate discussion on benefits, challenges, and pragmatic considerations for incorporating AI into office settings. Sessions were audio-recorded, transcribed, and analyzed using an iterative approach. Two primary constructs emerged. First, participants shared perspectives related to preferences and concerns regarding communication and interactions with the technology. Second, numerous conversations highlighted the dualistic nature of a system that collects large amounts of data; that is, the potential benefits for behavior change to improve health and the pitfalls of trust and privacy. Across both constructs, there was an overarching discussion related to the intersections of AI with the complexity of work performance. Numerous thoughts were shared relative to future AI solutions that could enhance the office workplace. This study’s findings indicate that the acceptability of AI in the workplace is complex and dependent upon the benefits outweighing the potential detriments. Office worker needs are complex and diverse, and AI systems should aim to accommodate individual needs.

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Competency-Industry Relatedness (C-IR) Framework for Sustained Business Growth in Startups during and beyond Pandemic: Myths and Lessons from Publicly Funded Innovative Startups

Sustainability ◽

10.3390/su13094632 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4632

Author(s):

Varun Gupta ◽

Luis Rubalcaba

Keyword(s):

Case Studies ◽

Business Environment ◽

Learning Curves ◽

The European Union ◽

Business Growth ◽

Product Lines ◽

Business Decisions ◽

Multiple Case ◽

Multiple Case Studies ◽

Industry Relatedness

Context: The coronavirus disease 2019 (COVID-19) pandemic led to a turbulent business environment, resulting in market uncertainties, frustrations, and rumors. Wrongly held beliefs—or myths—can hinder startups from turning new market opportunities into their favor (for example, by failing at diversification decisions) or undertaking wrong business decisions, e.g., diversifying in industries that have products of no real market value). Objectives: The objective of the paper is to identify the beliefs that drive the business decisions of startups in a pandemic and to isolate those beliefs that are merely myths. Further, this paper proposes strategic guidelines in the form of a framework to help startups make sound decisions that can lead to market success. Method: The two-step research method involved multiple case studies with five startups based in India, France, Italy, and Switzerland, to identify perceptual beliefs that drove strategic business decisions, followed by a case study of 36 COVID-19-solution focused startups, funded by the European Union (EU). The findings were validated through a survey that involved 102 entrepreneurs. The comparative analysis of two multiple case studies helped identify beliefs that were merely “myths”; myths that drove irrational strategic decisions, resulting in business failures. Results: The results indicate that startups make decisions in pandemic situations that are driven by seven myths, pertaining to human, intellectual, and financial resources. The decision on whether to diversify or continue in the same business operation can be divided into four strategic options of the Competency-Industry Relatedness (C-IR) framework: ignore, delay, phase-in, and diversify. Diversification in the same (or different industry) is less risky for startups if they have the skills, as needed, to diversify in related industries. Diversification in related industries helps startups leverage their experiences and learning curves (those associated with existing product lines) to adapt their existing products in new markets, or utilize their technologies to solve new problems via new products. The desired outcome for these startups should be sustainable business growth—to meet sustainability goals by contributing to the society and the economy. Conclusion: The C-IR framework is a strategic guide for startups to make business decisions based on internal factors, rather than myths. Accurately assessing skill diversity and the nature of new industries (or markets) will help startups leverage their existing resources optimally, without the need for (pricey) external funding. This will foster sustained business growth resulting in a nation economic development. Knowledge transfer from the Innovation ecosystem will further strengthen the C-IR framework effectiveness.

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Team Regulatory Focus and its Role for Idea Generation, Idea Implementation, and Innovative Performance: A Dynamic Perspective

The Journal of Creative Behavior ◽

10.1002/jocb.503 ◽

2021 ◽

Author(s):

Maike Hundeling ◽

Max Auerswald ◽

Kathrin Rosing

Keyword(s):

Regulatory Focus ◽

Idea Generation ◽

Innovative Performance ◽

Dynamic Perspective

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Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽

10.3390/pr9061070 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1070

Author(s):

Abdul Gani Abdul Jameel

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Functional Groups ◽

Average Error ◽

Ann Model ◽

Oh Groups ◽

Unseen Data ◽

Learning Capabilities ◽

Artificial Neural ◽

Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

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