Malachi Rayner-Philipson, Brian Sheil and Pin Zhang
A common design driver for pipe-jacking projects is the jacking force required to advance the tunnel boring machine and pipe string. Empirical methods are popular in industry but…
Abstract
Purpose
A common design driver for pipe-jacking projects is the jacking force required to advance the tunnel boring machine and pipe string. Empirical methods are popular in industry but are well known to lack accuracy, while there is a strong desire to supplement such approaches with robust data-driven techniques, typically small construction datasets present significant challenges.
Design/methodology/approach
To address this challenge, this paper develops a physics-constrained neural network predictive model for pipe-jacking forces. Information used as input into the model includes principal design information and soil type.
Findings
The physics constrained model was found to predict jacking force to a higher accuracy than current industry practice and better discern meaningful patterns in data than a purely data-driven artificial neural network. The results reveal promising performance for this initial dataset such that there is motivation, as a longer-term objective, to train the present approach on a more comprehensive drive database for more reliable and cost effective solutions for new projects.
Originality/value
Novel contributions include (a) a bespoke framework to constrain a neural network using a pipe-jacking mechanistic model which includes stoppage-induced friction increases, (b) built-in model uncertainty for greater confidence in model outputs, (c) new historical drive data for model training and (d) one-hot encoding of soil type as a model input. The model is calibrated and validated against 14 tunnel drives across four different sites with four distinctive ground types.
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Hai-xi Jiang and Nan-ping Jiang
A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains…
Abstract
Purpose
A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.
Design/methodology/approach
Based on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.
Findings
The research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.
Originality/value
This paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.
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Yucong Lao and Yukun You
This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder…
Abstract
Purpose
This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.
Design/methodology/approach
This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.
Findings
By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.
Originality/value
Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.
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Xiaoning Liang, Johanna Frösén and Yuhui Gao
Despite the availability of many metrics and tools for marketing performance measurement, the way in which firms use their marketing metrics remains underexplored. This study aims…
Abstract
Purpose
Despite the availability of many metrics and tools for marketing performance measurement, the way in which firms use their marketing metrics remains underexplored. This study aims to address this gap by empirically establishing the differing effects of the diagnostic and interactive uses of marketing metrics on firms’ market-sensing capability, contingent on competitive intensity and focus on market-related metrics.
Design/methodology/approach
This study builds on survey data collected from 210 Irish-based firms, complemented by 21 in-depth interviews with business managers. Survey data are analysed using regression analysis.
Findings
This study finds that firms using marketing metrics interactively to communicate organizational focus are better able to sense their markets, especially under high competition. The authors observe a positive impact of the interactive use of metrics on market-sensing capability, but a U-shaped impact of their diagnostic use, the magnitudes of which further depend on competitive intensity and firms’ focus on market-related metrics.
Research limitations/implications
This study provides a nuanced view of marketing performance measurement (MPM) practices within firms, particularly focussing on diagnostic versus interactive uses of marketing metrics. It also sheds further light on how two diverse uses of marketing metrics – diagnostic and interactive uses – influence a firm’s market-sensing capability. Moreover, the identification of boundary conditions also contributes to the discussion of contextuality in MPM, highlighting the importance of aligning a firm’s uses of marketing metrics with its business environment.
Practical implications
This study provides novel insights into how diverse uses of marketing metrics may benefit firms. The differing effects of diagnostic and interactive uses of marketing metrics on market sensing highlight a primary need for developing the latter and for using the former only with caution. It establishes that all firms would equally benefit from an interactive use of marketing metrics that is pivotal to improving their ability to anticipate, detect and sense market changes.
Originality/value
This study provides novel understanding of the role of marketing metric uses in firms’ market-sensing capability and contributes to the discussion of contextuality in marketing performance measurement. It highlights the importance of aligning a firm’s use of marketing metrics with its business environment.
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George Foster, Norm O'Reilly, Jim Best Devereux and Matias Shundi
This article seeks to enhance the understanding as to why head coaches and general managers (GMs) in the National Basketball Association (NBA) and the National Football League…
Abstract
Purpose
This article seeks to enhance the understanding as to why head coaches and general managers (GMs) in the National Basketball Association (NBA) and the National Football League (NFL) exit from their positions.
Design/methodology/approach
Three hypotheses were investigated using a series of quantitative and qualitative data from the past 30 years. The samples analyzed are comprised of 891 GM and coach annual observations for the NBA clubs and 949 GM and coach observations for the NFL clubs. Analyses include a logit analysis for coach exit/retention, a logit analysis for GM exit/retention and textual analysis via topic modeling via latent Dirichlet allocation.
Findings
Results show a correlation between a coach exiting and a GM exiting simultaneously, thus amplifying the importance of these two roles in enhancing or destroying the success of a club and supporting the need for a deeper understanding of both roles, particularly the GM. The results further highlight cultural differences across clubs in terms of GM and coach turnover, a factor that often is heavily influenced by club ownership.
Originality/value
The results support the role of owners in exits, confirm the importance of winning in avoiding an exit, find a high level of interrelationship between GM and coach exits and show that past culture of firings influences future exit decisions.