Keith Jones, Api Desai, Noel Brosnan, Justine Cooper and Fuad Ali
The purpose of this paper is to present results of an action research addressing climate change adaptation of selected social housing stock in the UK. Climate change continues to…
Abstract
Purpose
The purpose of this paper is to present results of an action research addressing climate change adaptation of selected social housing stock in the UK. Climate change continues to pose major challenges to those responsible for the management of built assets. The adaptation required to address long-term building performance affected by climate change rarely get prioritised above more immediate, short-term needs (general built asset management needs).
Design/methodology/approach
The study adopts an in-depth participatory action research with a London-based social landlord and integrates climate change adaptation framework and performance-based model established through author’s previous research projects.
Findings
A staged process for including adaptation measures in built asset management strategy is developed along with metrics to analyse the performance of the housing stock against climate change impact of flooding. The prioritisation of adaptation measure implementation into long-term built asset management plans was examined through cost-based appraisal.
Research limitations/implications
The research was carried out with a singular organisation, already acquainted with potential climate change impact, vulnerability and adaptive capacity assessment. The process adopted will differ for similar organisation in the sector with different settings and limited working knowledge of climate change impact assessment.
Practical implications
The paper concludes with a ten-step process developed as an aide memoir to guide social landlords through the climate change adaptation planning process.
Originality/value
In addition to the practical results from the study, the paper outlines a novel process that integrates resilience concepts, risk framing (to climate change impact) and performance management into built asset management (maintenance and refurbishment) planning.
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Keith Jones, Api Desai, Mark Mulville and Aled Jones
The purpose of this paper is to present an alternative approach to facilities and built asset management adaptation planning to climate change based on a hybrid…
Abstract
Purpose
The purpose of this paper is to present an alternative approach to facilities and built asset management adaptation planning to climate change based on a hybrid backcasting/forecasting model. Backcasting envisions a future state and examines alternative “pathways of approach” by looking backwards from the future state to the present day. Each pathway is examined in turn to identify interventions required for that pathway to achieve the future state. Each pathway is reviewed using forecasting tools and the most appropriate is selected. This paper describes the application of this approach to the integration of climate change adaptation plans into facilities and built asset management.
Design/methodology/approach
The researchers worked with various stakeholders as part of a participatory research team to identify climate change adaptations that may be required to ensure the continued performance of a new educational building over its life cycle. The team identified 2020, 2040 and 2080 year end-goals and assessed alternative pathways of approach. The most appropriate pathways were integrated into the facilities and built asset management plan.
Findings
The paper outlines a conceptual framework for formulating long term facilities and built asset management strategies to address adaptation to climate change.
Research limitations/implications
The conceptual framework is validated by a single research case study, and further examples are needed to ensure validity of the approach in different facilities management contexts.
Originality/value
This is the first paper to explore backcasting principles as part of facilities and built asset management planning.
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Anoop Vasu, Jerry Chung, Cory Padfield and Ravi Desai
The brake reaction test performed on a rear axle assembly revealed that the brake flange weld could not sustain the load needed to pass the minimum requirement of the test…
Abstract
Purpose
The brake reaction test performed on a rear axle assembly revealed that the brake flange weld could not sustain the load needed to pass the minimum requirement of the test. Evaluation of the failure mode indicated that the fracture of the weld originated at the root of the weld and cracked through the fusion zone of the weld instead of cracking through base material (toe failure). The paper aims to discuss these issues.
Design/methodology/approach
A computational methodology is presented to quantify the critical parameters to prevent throat failure. The torsion dominated loading created high in-plane shear stress on the weld which can contribute significantly to the premature failure.
Findings
The failure through the fusion zone, often termed as weld throat/root failure, was not accounted for during the design phase by numerical simulation which led to the wrong conclusion that the design will pass the test requirement. Although weld sizing and weld penetration depth can explain such unexpected failure modes, fatigue life of this particular failure was still over-predicted using the Master SN curve formulation of structural stress approach which is well established for Mode I type of failure. Accounting for the shear component in the structural stress approach led to good correlation with the test specimen. Weld throat depth is a significant parameter contributing to throat failure.
Practical implications
The failure of the weld joining the brake flange and the tube of an axle is a high severity failure mode which can result in loss of vehicle control and injury or death and hence the failure should be prevented at any cost.
Originality/value
Most of the previous work of welded components relates to Mode I loading. There is very few research performed to discuss the Mode III loading and failure. This research illustrates the importance of considering the throat failure mode and quantifies the weld parameters to prevent such failures in design applications.
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Osamah M. Al-Qershi, Junbum Kwon, Shuning Zhao and Zhaokun Li
For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of…
Abstract
Purpose
For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.
Design/methodology/approach
With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.
Findings
XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.
Research limitations/implications
This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.
Practical implications
Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.
Originality/value
Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.
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This paper aims to provide graduate students, researchers, flare design and operational oil companies with an overview on the flare incidents.
Abstract
Purpose
This paper aims to provide graduate students, researchers, flare design and operational oil companies with an overview on the flare incidents.
Design/methodology/approach
The design and operation of flares have been the subject of research and concern to the academicians and to flare design and operational oil companies. The flare incidents have been collected from several sources such as the technical, general articles, internet web sites, and internal reports. The flare incident types and causes have been reviewed. This paper presents an overview on the flare incidents. The paper presents a flare incident at an oilfield. The paper summarizes the causes and the results of the incident.
Findings
A lesson was learned that a flashback scenario could occur at the high‐pressure flares unless the flare system was designed properly. A flare system should be designed according to the standard codes. American Petroleum Institute API 521 recommended guidelines on the design of the relief system. John Zinc Company and GKN also recommended guidelines for the installation and protection of flares.
Originality/value
This paper presents the flare incident types and causes. The paper also presents an overview on a flare incident at an oilfield. The paper summarizes the causes and the results of the incident. This information can be beneficial in order to prevent similar flare incidents.
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Giuseppe Festa, Ashutosh Kolte, Maria Rosaria Carli and Matteo Rossi
This study aims to access, analyze and highlight opportunities and problems of the Indian pharmaceutical sector in the broader national health-care industry. The recent changes in…
Abstract
Purpose
This study aims to access, analyze and highlight opportunities and problems of the Indian pharmaceutical sector in the broader national health-care industry. The recent changes in the field, at the institutional and corporate levels, have placed India in the spotlight of the global pharmaceutical market, but several threats and weaknesses could limit this expansion.
Design/methodology/approach
Descriptive and inferential analyses have been based on empirical data extracted from authenticated data sources. Subsequently, a narrative strengths, weaknesses, opportunities and threats analysis was performed based on the results of prior investigations and on qualitative data that were retrieved from a marketing intelligence examination to generate an overall scenario analysis.
Findings
Indian pharmaceutical companies have faced several challenges on various fronts. In the home market, drug prices are controlled by the drug price control order; therefore, there is strong pressure on revenues and subsequently on costs. In the international market, threats derived from pharmaceutical multinational companies are emerging as tough obstacles to overcome.
Practical implications
More focus on patents for innovative drugs is required, instead of concentrating primarily on generic drugs. There is a need for policymakers to work on the sustainability and development of the industry, while the companies must redesign their orientation toward enhancing innovation capabilities. In addition, at the level of corporate strategy, firms should establish collaborations and alliances and expand their industrial marketing vision.
Originality/value
This study provides a global overview of the potential growth and development of the Indian pharmaceutical sector, comparing it with internal trends and external competition. The most relevant contribution of the research relies on the shift to innovative production that Indian companies must adopt (after years of focusing only on generic drugs), and in this vein, appropriate industrial marketing solutions are indispensable.
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Sajad Noorbakhsh, Aurora Castro Teixeira and Ana Brochado
Refugee entrepreneurship is increasingly viewed as a “silver bullet” being able to promote host countries’ economic performance and enable the successful integration of refugees…
Abstract
Purpose
Refugee entrepreneurship is increasingly viewed as a “silver bullet” being able to promote host countries’ economic performance and enable the successful integration of refugees. This study aims to identify the main determinants of entrepreneurial intentions of refugees in Portugal based on the underdog theory.
Design/methodology/approach
In this study, the authors scrutinize the entrepreneurial intentions of refugees living in Portugal, an overlooked context, using a purpose-built inquiry responded to by 41 refugees and resorting to fuzzy-set qualitative comparative analysis, complemented with partial least squares path modeling.
Findings
Some important results are worth highlighting: the entrepreneurial intentions of the respondent sample of refugees living in Portugal are high; the theoretical arguments underlying the underdog or challenge-based entrepreneurship theory are validated in the context of the respondent sample; and psychological related factors associated with the more standard explanations of entrepreneurial intentions constitute necessary conditions for high refugee entrepreneurial intentions.
Originality/value
Entrepreneurial intentions to launch a business have been discussed in the entrepreneurship literature vastly, but it has not yet received much attention when focusing on refugees, often identified as underdogs (potential) entrepreneurs. This study contributes to the literature by testing the challenge-based entrepreneurship theory to identify the primary factors influencing refugee entrepreneurial intentions.
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Rongying Zhao, Weijie Zhu, He Huang and Wenxin Chen
Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively…
Abstract
Purpose
Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.
Design/methodology/approach
This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.
Findings
The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.
Originality/value
Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.
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Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah and Mousa Albashrawi
This study aims to investigate public sentiment toward economic stimulus using textual analysis. Specifically, it analyzes Twitter’s public opinion, emotion-based sentiment and…
Abstract
Purpose
This study aims to investigate public sentiment toward economic stimulus using textual analysis. Specifically, it analyzes Twitter’s public opinion, emotion-based sentiment and topics related to COVID-19 economic stimulus packages.
Design/methodology/approach
This study applies natural language processing techniques, such as sentiment analysis, t-distributed stochastic neighbor embedding and semantic network analysis, to a global data set of 88,441 tweets from January 2020 to December 2021 extracted from the Twitter platform, discussing COVID-19 economic stimulus packages.
Findings
Results show that in the fourth quarter of 2021, there is a declining trend of positive sentiment (−5%) and an increasing trend of negative sentiment (14%), which may indicate the perceived inadequacy of COVID-19 stimulus measures. Topic modeling identifies seven topics, highlighting the necessity of stimulus in the education sector.
Practical implications
The big-data findings of this study provide a better understanding of public sentiment about economic stimulus for regulators and policymakers, which can help in formulating more effective fiscal and monetary policies.
Originality/value
Public sentiment is a significant concern for regulators because of its associated ambiguity, such as how to design stimulus packages and evaluate the effectiveness of previous measures. This study applies natural language processing, contributing to the growing literature on designing effective economic stimulus.