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Open Access
Article
Publication date: 28 November 2022

Ruchi Kejriwal, Monika Garg and Gaurav Sarin

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both…

Abstract

Purpose

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

Design/methodology/approach

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Findings

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

Originality/value

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Details

Vilakshan - XIMB Journal of Management, vol. 21 no. 1
Type: Research Article
ISSN: 0973-1954

Keywords

Open Access
Article
Publication date: 15 December 2023

Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…

Abstract

Purpose

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

Design/methodology/approach

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

Findings

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

Practical implications

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

Originality/value

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Details

International Journal of Productivity and Performance Management, vol. 73 no. 11
Type: Research Article
ISSN: 1741-0401

Keywords

Abstract

Details

Police Responses to Islamist Violent Extremism and Terrorism
Type: Book
ISBN: 978-1-83797-845-8

Article
Publication date: 12 September 2023

Myriam Ertz, Shashi Kashav, Tian Zeng and Shouheng Sun

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This…

Abstract

Purpose

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

Design/methodology/approach

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

Findings

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

Originality/value

The study provides an up-to-date review of extant research pertaining to SLCA.

Open Access
Article
Publication date: 25 May 2023

Suchismita Swain, Kamalakanta Muduli, Anil Kumar and Sunil Luthra

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships…

Abstract

Purpose

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Design/methodology/approach

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

Findings

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Practical implications

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

Originality/value

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Details

International Journal of Industrial Engineering and Operations Management, vol. 6 no. 2
Type: Research Article
ISSN: 2690-6090

Keywords

Open Access
Article
Publication date: 29 February 2024

Mehroosh Tak, Kirsty Blair and João Gabriel Oliveira Marques

High levels of child obesity alongside rising stunting and the absence of a coherent food policy have deemed UK’s food system to be broken. The National Food Strategy (NFS) was…

Abstract

Purpose

High levels of child obesity alongside rising stunting and the absence of a coherent food policy have deemed UK’s food system to be broken. The National Food Strategy (NFS) was debated intensely in media, with discussions on how and who should fix the food system.

Design/methodology/approach

Using a mixed methods approach, the authors conduct framing analysis on traditional media and sentiment analysis of twitter reactions to the NFS to identify frames used to shape food system policy interventions.

Findings

The study finds evidence that the media coverage of the NFS often utilised the tropes of “culture wars” shaping the debate of who is responsible to fix the food system – the government, the public or the industry. NFS recommendations were portrayed as issues of free choice to shift the debate away from government action correcting for market failure. In contrast, the industry was showcased as equipped to intervene on its own accord. Dietary recommendations made by the NFS were depicted as hurting the poor, painting a picture of helplessness and loss of control, while their voices were omitted and not represented in traditional media.

Social implications

British media’s alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system. Media firms should move beyond tropes of culture wars to discuss interventions that reform the structural causes of the UK’s broken food systems.

Originality/value

As traditional media coverage struggles to capture the diversity of public perception; the authors supplement framing analysis with sentiment analysis of Twitter data. To the best of our knowledge, no such media (and social media) analysis of the NFS has been conducted. The paper is also original as it extends our understanding of how media alignment with free market economic thinking has implications for food systems reform, as it deters the government from acting and relies on the invisible hand of the market to fix the system.

Details

British Food Journal, vol. 126 no. 13
Type: Research Article
ISSN: 0007-070X

Keywords

Open Access
Article
Publication date: 15 February 2024

Pertti Vakkari

The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the…

Abstract

Purpose

The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change.

Design/methodology/approach

The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

Findings

The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies.

Originality/value

The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.

Details

Journal of Documentation, vol. 80 no. 7
Type: Research Article
ISSN: 0022-0418

Keywords

Open Access
Article
Publication date: 24 January 2024

Carlo Giannetto, Angelina De Pascale, Giuseppe Di Vita and Maurizio Lanfranchi

Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both…

Abstract

Purpose

Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both as a fresh product and as processed food. However, as with many other products, the consumption of fruits and vegetables and, more specifically apples, has been drastically affected by the first lockdown in 2020. In this project, the authors investigate whether the change in consumption habits had long-lasting consequences beyond 2020 and what are the main eating motivations, food-related behavior and socio-demographic affecting the consumption of fruits and vegetables after the pandemic.

Design/methodology/approach

The authors ran two online surveys with 1,000 Italian consumers across a year (from October 2021 to December 2022). In the study, participants answered questions about their consumption habits and their eating motives. Out of 1,000 consumers, the authors included in the final analysis only the participants who answered both surveys, leaving a final sample of 651 consumers.

Findings

The results show that participants have allocated more budget to fruit and vegetables after the lockdown than before it. Moreover, consumers reported an average increase in the consumption of apples. However, the increase was more pronounced for people aged between 30 and 50 years old and identified as female. After showing the difference across time, a cluster analysis identified three main segments that differ in their eating motives, place of purchase and area of residence.

Practical implications

Overall, the results contribute to a better understanding of how the global pandemic is still affecting people's daily life. Moreover, the findings can be used to guide the marketing and communication strategies of companies in the food sector.

Originality/value

To the best of the authors' knowledge, this is the first study that investigates changes in the consumption of fruits and vegetables, and, more specifically, apples, in Italy more than one year after the beginning of the COVID-19 pandemic. Moreover, the study proposes a classification of consumers based on their habits in a time frame during which the COVID-19 wave was at its bottom which is not currently present in the literature.

Details

British Food Journal, vol. 126 no. 13
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 13 November 2023

Urmila Jagadeeswari Itam and Uma Warrier

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the…

Abstract

Purpose

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the COVID-19 pandemic accelerating current trends in work-from-everywhere (WFE) research. This article presents a systematic literature review of WFE research from 1990 to early 2023 to understand the transformation of the field.

Design/methodology/approach

The Web of Science database was used to conduct this review based on rigorous bibliometric and network analysis techniques. The prominence of the research studied using SPAR-4-SLR and a collection of bibliometric techniques on selected journal articles, reviews and early access articles. Performance and keyword co-occurrence analysis form the premise of cluster analysis. The content analysis of recently published papers revealed the driving and restraining forces that help define and operationalize the concept of WFE.

Findings

The major findings indicate that the five established and accelerated trends from cluster analysis are COVID-19 and the pandemic, telework(ing), remote working, work from home and well-being and productivity. Driving and restraining forces identified through content analysis include technological breakthroughs, work–life integration challenges, inequality in the distribution of jobs, gender, shifts in industry and sector preferences, upskilling and reskilling and many more have been published post-COVID in the restraining forces category of WFE.

Practical implications

A key contribution of this pioneering study of “work from everywhere” is the linking of the bibliometric trends of the past three decades to the influencing and restraining factors during the pandemic. This study illustrates how WFE could be perceived differently post-COVID, which is of great concern to practitioners and future researchers.

Originality/value

A wide range of publications on WFE and multiple synonyms can create confusion if a systematic and effective system does not classify and associate them. This study uses both bibliometric and scientometric analyses in the context of WFE using systematic literature review (SLR) methods.

Details

International Journal of Manpower, vol. 45 no. 1
Type: Research Article
ISSN: 0143-7720

Keywords

Article
Publication date: 28 November 2023

Shiqin Zeng, Frederick Chung and Baabak Ashuri

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face…

Abstract

Purpose

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

Design/methodology/approach

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

Findings

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

Originality/value

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

Details

Built Environment Project and Asset Management, vol. 14 no. 2
Type: Research Article
ISSN: 2044-124X

Keywords

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