Atika Qazi, Ram Gopal Raj, Glenn Hardaker and Craig Standing
The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is…
Abstract
Purpose
The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.
Design/methodology/approach
A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.
Findings
The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.
Research limitations/implications
The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.
Originality/value
Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.
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Hardik Shah and Raj Gopal
With rapid industrialization and population growth in the urban and rural areas in India, the demand for public transport has risen sharply. In the fast changing scenario in the…
Abstract
Purpose
With rapid industrialization and population growth in the urban and rural areas in India, the demand for public transport has risen sharply. In the fast changing scenario in the public transit sector, the role of a bus depot manager (DM) has also undergone substantial transformation. This paper aims to identify and gauge the organizational and individual training needs of DMs, necessary to design and delivery of effective training.
Design/methodology/approach
Primary data were collected by visiting three bus depots, nine in‐depth interviews with the top management team, 15 in‐depth interviews with DMs, performance data of 15 DMs and a survey of 114 DMs, using open ended questions. After primary analysis a ranking order instrument was designed and administered to 114 DMs.
Findings
The results explored current and future training requirements for the role of bus DMs based on their preference and priority. Seven differentiating competencies and ten priority training areas were identified using “priority index”. Further suggestions have been made for enhancing training effectiveness.
Practical implications
The paper provides practical insights into how to conduct training needs analysis for bus DMs using differentiating competencies and priority index. Training managers may use such tools to identify training gaps in different roles while designing effective training strategies. It provides insights into the role of bus DMs, current and future role requirements, seven differentiating competencies and training gaps in the role of a DM.
Research limitations/implications
The findings are presented based on one single role in one organization only, i.e. DMs of Gujarat State Road Transport Corporation (GSRTC). Replication studies should examine different roles across different functions.
Originality/value
This paper investigates how to identify training needs for depot managers using differentiating competencies and priority index. It provides inferences on how to align business objectives, individual performance and training needs. The tools used are ready‐to‐use and replicable for different roles in medium and large MNCs.
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This paper aims to analyze the agile manufacturing practices in small and medium enterprises (SMEs) within the auto-ancillary sector spread across eastern India. Using statistical…
Abstract
Purpose
This paper aims to analyze the agile manufacturing practices in small and medium enterprises (SMEs) within the auto-ancillary sector spread across eastern India. Using statistical method, a number of crucial insights have been captured and discussed in detail.
Design/methodology/approach
A structural equation model (SEM) encapsulating pertinent agile manufacturing attributes and enablers as the focal construct is conceptualized and validated in this research. The SEM model is evolved in a manner that agile attributes and agile enablers are modeled as latent dependent and manifest variable, respectively.
Findings
A number of key hypothesis entailing interactions of key agility enablers, i.e. manufacturing responsiveness agility, manufacturing strategy agility, workforce agility, technology agility, manufacturing management agility, etc. are conceptualized and validated.
Originality/value
The authors specifically focus on evolving agile manufacturing framework (characterized by agility enablers and attributes) that lies at the intersection of SMEs, automotive sector and an emerging economy.
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Keywords
Marketing, Pricing, Strategic marketing.
Abstract
Subject area
Marketing, Pricing, Strategic marketing.
Study level/applicability
The case is developed for an MBA-level program.
Case overview
In May 2017, the telecom industry in India witnessed an intense price war over 4G (fourth generation) data prices. Gopal Vittal, CEO of Bharti Airtel was exploring various options on how best to respond to the situation. He had to take a final call regarding Bharti Airtel’s marketing team’s counter move to tackle this price war by Jio – should Bharti Airtel ignore it, accommodate it or retaliate with even lower prices? Bharti Airtel strongly believed that Jio pricing structure had violated “fair pricing” norms, and its pricing was anti-competitive. It had filed a case with the Telecom Regulatory Authority of India (TRAI) and the Competition Commission of India (CCI) to restrain Jio from further giving “free” promotional offers and penalize it for it. Could the legal recourse by Bharti Airtel dampen Jio’s consistent subscriber growth rate?
Expected learning outcomes
The case provides the students with an insight into how the competition focused on pricing happens in the telecom industry. The pricing war affects the profit margin of all competing companies. It changes the customer reference point for evaluating the competing products and services. The students would also learn practical applications of positive-sum pricing, pricing war, fair pricing and legal aspects of pricing. This case provides the students with an opportunity to understand the pricing war and how to respond to it in a particular situation; understand positive-sum pricing and negative-sum pricing in telecom industry context; understand legal aspects of pricing; and how to leverage data for gaining newer customer insights.
Supplementary materials
Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.
Subject code
CSS 8: Marketing.
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Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are…
Abstract
Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.
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Gopal Krushna Gouda and Binita Tiwari
This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting…
Abstract
Purpose
This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.
Design/methodology/approach
Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.
Findings
The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.
Practical implications
The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.
Originality/value
This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.
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Bee Lian Song, Chee Yoong Liew, Jye Ying Sia and Kanesh Gopal
Young consumers are increasing using electronic word-of-mouth (eWOM) in travel social networking sites to make purchase decisions. This paper aims to test the extended Information…
Abstract
Purpose
Young consumers are increasing using electronic word-of-mouth (eWOM) in travel social networking sites to make purchase decisions. This paper aims to test the extended Information Adoption Model (IAM) that places perceived usefulness and information adoption as consequences of argument quality, source credibility, information quantity and emotive word comprehension, and as an antecedent of purchase intentions.
Design/methodology/approach
Data are collected through survey questionnaire from 405 hotel young customers in Malaysia, who had experienced travel social networking sites. The hypothesized relationships were analysed using structural equation modelling.
Findings
The results show that argument quality, source credibility, information quantity and emotive word comprehension have positive effect on the perceived usefulness of eWOM. Perceived usefulness has positive influence on the information adoption of eWOM, which in turn predicts the young consumers’ purchase intentions.
Research limitations/implications
The present study strengthens and advances the existing literature on tourism, social media and marketing by offering an extension to the IAM. The proposed extended model of IAM is verified and applied effectively in the context of eWOM for travel social networking sites.
Practical implications
Practitioners and marketers of travel social networking sites can improve the usability and effectiveness of eWOM to attract more young consumers.
Originality/value
The study contributes to the extension of IAM by adding information quantity and emotive word comprehension. This research validated the significant roles of eWOM argument quality and source credibility in predicting the information usefulness of eWOM.
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Rohit Raj, Vimal Kumar and Bhavin Shah
Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline…
Abstract
Purpose
Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.
Design/methodology/approach
Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.
Findings
To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.
Research limitations/implications
The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.
Practical implications
In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.
Originality/value
The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).
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Selladurai Pitchaimuthu, Jitesh J. Thakkar and P.R.C. Gopal
Risk management in defence aircraft industry has considerable interest among academics and practitioners. The purpose of this paper is to develop interactions among risk factors…
Abstract
Purpose
Risk management in defence aircraft industry has considerable interest among academics and practitioners. The purpose of this paper is to develop interactions among risk factors dimensions (RFDs) and inspect the importance relationship among the performance measures in Indian aircraft industry and, finally, understand the effect of involvements provided by the managerial team on risk reduction process.
Design/methodology/approach
An extensive literature review was carried out to identify 26 risk parameters and 13 performance measure indices relevant for an aircraft industry. Survey method was used to obtain the importance of these parameters and measures. Further, these factors are grouped into five risk dimensions based on the brain storming session by the project managers. Initially, Risk factors for defense aircraft industry (RFDs) analyzed by Interpretative structural model (ISM) to know the contextual relationship among the RFDs and then applied Interpretive ranking process (IRP) to inspect the pre-eminence relationship among them. Finally, SD is applied to understand the effect of involvements provided by the managerial team on risk reduction process.
Findings
Government policy and legal RFDs has emerged as the key driving RFDs. In IRP modelling, technology RFD has emerged as more influential RFD which is the more relevant factor with respect to performance measure indices and this result is supported by detailed sensitivity analysis of system dynamic model.
Originality/value
The outcomes of this research can help project management team to identify the high severity risk factors which need immediate risk reduction/mitigation action.
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Gahana Gopal C., Yogesh B. Patil, Shibin K.T. and Anand Prakash
The purpose of this paper is to formulate frameworks for the drivers and barriers of integrated sustainable solid waste management (ISSWM) with reference to conditions prevailing…
Abstract
Purpose
The purpose of this paper is to formulate frameworks for the drivers and barriers of integrated sustainable solid waste management (ISSWM) with reference to conditions prevailing in India.
Design/methodology/approach
A multi-phased approach was adopted in this paper to come up with the conceptual framework of the drivers and barriers of ISSWM. In the first phase, drivers and barriers of ISSWM were identified based on a systematic literature review process. In the second phase, 25 experts having 15 plus years of experience in the field of sustainable development and environmental management were consulted to get their opinion. Validation and understanding of the interrelationship among the selected drivers and barriers were done based on the insights from expert interviews. And in the final phase, structural self-interaction matrix and transitive links are defined based on the expert opinion to come up with the theoretical frameworks of drivers and barriers of ISSWM.
Findings
Findings reveal the importance to have a system view point approach by giving equal importance to social, environmental and economic pillars of sustainability along with the technology component to effectively and sustainably manage the solid waste disposal. Institutional effectiveness and the robust policy and frameworks are the two variables found to have the highest driving power. Poor social values and ethics, huge population and illiteracy are the three most critical barriers faced by developing nations in achieving the sustainability practices in the solid waste management. The proposed frameworks of drivers and barriers of ISSWM will definitely help policy makers to effectively manage the sustainable waste management practices for developing economies by focusing on the key variables listed out.
Research limitations/implications
One of the limitations is in the use of very limited sample size in the study. Another limitation is that total interpretive structural modeling fails to come up with the relative weightings of drivers and barriers used in the study. These limitations can be overcome by extending the research by using a semi-structured questionnaire survey with higher sample size for the empirical validation of the model.
Practical implications
This research will help to clearly understand the framework of drivers and barriers of variables and their hierarchical level based on the driving power and dependence. Since such articles focusing on the conceptual frameworks of drivers and barriers of ISSWM are found to be very scant, this paper will equally help academicians and waste management professionals to understand the concepts deeply, by getting answers to the fundamental questions of “what,” “why” and “how.” Developed framework of drivers explicitly shows the need to attain financial stability through the commercialization of the waste management initiatives, which will help to reduce burden on various governmental institutions. Commercialization opportunities will also help to have more successful start-up ventures in solid waste management domain that can provide improved employment opportunities and hygiene environment in the developing nations like India.
Originality/value
Based on the authors’ best knowledge, there is hardly any article that explicitly explains the conceptual frameworks of the drivers and barriers of ISSWM by considering the conditions prevailing in developing countries like India. And thus, this can be considered as one of the unique research attempts to build a clear conceptual framework of ISSWM. The study contributes significantly to the existing literature body by clearly interpreting the interrelationships and the driving power and dependence of variables of ISSWM.