Divya Sharma, M. Vimalkumar, Sirish Gouda, Agam Gupta and Vignesh Ilavarasan
Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from…
Abstract
Purpose
Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.
Design/methodology/approach
The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.
Findings
Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.
Originality/value
This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.
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Keywords
Anuj Batta, Mohina Gandhi, Arpan Kumar Kar, Navin Loganayagam and Vignesh Ilavarasan
Blockchain technology has fascinated researchers and industry professionals. Since its birth, the attention for blockchain has been exponentially increasing, however, most of the…
Abstract
Purpose
Blockchain technology has fascinated researchers and industry professionals. Since its birth, the attention for blockchain has been exponentially increasing, however, most of the industries are still skeptical in adoption for value creation. The purpose of this study is to analyze the actual level of implementation and diffusion of blockchain technology within the logistics and transportation industry by comparing and using the collective intelligence of academic literature and industry practices of implementation of blockchain in this domain.
Design/methodology/approach
This study uses the methodology of systematic literature review along with inductive reasoning. The systematic literature review of academic and industry frontiers together has brought a bigger and real picture into consideration.
Findings
The results highlight that, within the transportation sector, currently there is a very low diffusion of blockchain, although applications show immense promises for the future. The various application where blockchain technology can make a significant impact are also identified.
Research limitations/implications
Due to the early stage of experimentation with blockchain technology, high-quality data which is relevant to the optimized usage of this technology in the logistics and transportation industry is not available.
Practical implications
The study will help the practitioners in identifying additional avenues in which they could implement blockchain for the effectiveness, efficiency and growth of the logistics and transportation industry.
Originality/value
The analysis of mixed sources of information for undertaking systematic literature review by assessing academic and trade publications is a novelty of this study.
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Sonali Shankar, Sushil Punia and P. Vigneswara Ilavarasan
Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of…
Abstract
Purpose
Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.
Design/methodology/approach
A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.
Findings
The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”
Originality/value
A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).
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Maciel M. Queiroz, Charbel José Chiappetta Jabbour, Ana Beatriz Lopes de Sousa Jabbour, Susana Carla Farias Pereira and Julio Carneiro-da-Cunha
Peace engineering and compassionate operations can unlock the potential of emerging technologies for social good. This work aims to investigate the integration of peace…
Abstract
Purpose
Peace engineering and compassionate operations can unlock the potential of emerging technologies for social good. This work aims to investigate the integration of peace engineering and compassionate operations by proposing an integrative framework and identifying the main drivers regarding social good, considering the Sustainable Development Goals (SDGs) landscape.
Design/methodology/approach
The authors used a two-stage methodology by employing a narrative literature review in the first stage to identify the relationships and drivers and propose an original framework. In the second stage, the authors utilized an expert panel to validate the framework’s drivers.
Findings
The authors identified five main categories related to peace engineering and compassionate operations, which were then used to support the categorization of the drivers. In the second stage, the authors validated the drivers with a panel of academicians and experienced industry practitioners.
Practical implications
The proposed framework can provide insightful directions for practitioners and governments to develop strategies and projects in different contexts, including humanitarian logistics, climate change crises, supply chain disruptions, etc.
Originality/value
This work makes unique contributions by reinvigorating an amalgamation of the peace engineering and compassionate operations arenas and their integration with the SDGs to enable enhanced social good, supported by cutting-edge technologies. Thus, this framework’s contributions encompass essential theoretical, managerial, and social implications.