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This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models.

First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared.

The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets.

In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

The present study is aimed at understanding the survival strategies of Subsistence-type Rural Independent retailers, henceforth called SRIs, in the Bottom-of-the-Pyramid (BoP) markets of developing economies through a qualitative study. SRIs constitute a pivotal channel of distribution of goods to BoP consumers living in the rural areas of developing economies. A process of long interviews was chosen for data gathering to allow SRIs to go into details to allow them to expound upon their beliefs, life-situations, and societal norms. Narratives were collected verbatim from SRIs. The concept of socio-economic embeddedness is used as the central concept to interpret and connect the elements, discerned from the narratives, into a conceptual framework. The aforesaid theory combines the neo-classical economic concept of utility maximization with behavioral economics and economic sociology. The analysis of the narratives is interpretive against the identified elements of the concept of economic embeddedness. The survival strategies of SRIs seem to stem from sociological, psychological, and utility-maximizing behaviors. The elements of SRIs’ responses to its environment provide valuable insights into their purchase motivations.

The purpose of this paper is to investigate the capabilities of social entrepreneurship (SE) firms and how they achieve competitive advantage while engaging in social value creation. The authors employ a business model perspective to understand the (self-) sustaining mechanism for social good.

The authors carry out an in-depth investigation of three SE ventures. The authors analyse the history of these ventures to determine how they achieved competitive advantage. The cases are analysed based on the internal development in the context of environmental support.

The authors find that SE ventures, like all other organisations, achieve competitive advantage based on available resources such as reputation and network of the founder, managerial experience and other corporate resources within the firm. The authors also find that the competitive advantage often comes from innovate usage – a practice that is reinforced by the support from institutional environment.

Due to the case study approach, the research is aimed at theoretical development within the social business literature. The approach makes it difficult for the findings to be universally generalisable. The authors therefore envisage future researchers to test the theoretical propositions.

Based on the analysis, the authors conclude that distinct capabilities of social businesses help them achieve competitive advantage, and that policy makers should institutionally support these ventures. The findings stress the importance of resources in sustainability and effectiveness of emerging market SE.

The application of a business model perspective in SE is unique, and advances the understanding of social businesses from a strategic management perspective.

Magnetic permeability variations of ferromagnetic materials under elastic stress offer the potential to monitor tension based on the inverse magnetostrictive effect. The purpose of this paper is to propose an innovative self-inductance tension eddy current sensor to detect tension.

The effectiveness of conventional elasto-magnetic (EM) sensor is limited during signal detection, due to its complex sensor structure, which includes excitation and induction coils. In this paper, a novel self-inductance tension eddy current sensor using a single coil is presented.

The output signal was analyzed through oscilloscope in the frequency domain and via self-developed data logger in the time domain. Experimental results show the existence of a linear relationship between voltage across the sensor and tension. The sensor sensitivity is dependent on operating conditions, such as current and frequency of the input signal.

The self-inductance sensor has great potential for replacing conventional EM sensor due to its low cost, simple structure, high precision and good repeatability in tension detection.

A spilt sleeve structure provides a higher permeability path to magnetic field lines than a non-sleeve structure, thus reducing the loss of magnetic field. The self-developed data logger improves sensitivity and signal-to-noise ratio of sensor. The novel sensor, as a replacement of the EM sensor, can easily and accurately monitor the tension force.

The purpose of this paper is to explore and evaluate the performance comparison of carbon nanotubes (CNT) and nickel silicide (NiSi) nanowires interconnects as prospective alternatives to copper wire interconnects.

The increasing resistivity of the copper wire with scaling and rising demands on current density drives the need for identifying new wiring solutions. This paper explores the various alternatives to copper. The metallic bundle CNTs and NiSi nanowires are promising candidates that can potentially address the challenges faced by copper. This paper analyzes various electrical models of carbon nanotube and recently introduced novel interconnect solution using NiSi nanowires.

The theoretical studies proves CNTs and NiSi nanowires to be better alternatives against copper on the ground of performance parameters, such as effective current density, delay and power consumption. NiSi nanowire provides highest propagation speed for short wire length, and copper is the best for intermediate wire length, while bundle CNTs is faster for long wire length. NiSi nanowire has lowest power consumption than copper and CNTs.

This paper investigates, assess and compares the performance of carbon nanotubes (CNT) and NiSi nanowires interconnects as prospective alternatives to copper wire interconnects in future VLSI chips.

A significant increase in the number of private sector banks has intensified the level of competition in the Indian banking industry (IBI). This increase in the number of banks has a considerable impact on the existing players, which calls for prioritizing customer satisfaction (CS) and enhancing bank reputation (BR). Our study seeks to investigate the enablers of CS and BR in the IBI.

The study adopted a cross-sectional design for gathering responses from retail bank customers across the selected banks through a structured questionnaire. Structural equation modeling (SEM) was utilized to evaluate direct and indirect linkages among the identified constructs by examining mediating and moderating effects.

The study puts forward crucial antecedents of CS and BR. The findings exhibit that perceived trust (PT) and relationship commitment (RC) magnify CS and BR, respectively, while CS amplifies repurchase intention (RI). The study advances that BR and CS partially mediate between the underlying constructs. In addition, fairness and risk exhibit moderating effects between CS and customer repurchase intention (CRI) and BR and CRI.

The study illustrates the crucial enablers of BR, CS and CRI that may assist banking professionals in enriching customer experience and holding on to their customers.

There is a shortage of research on RC, service innovation (SI) and BR in the IBI. Accordingly, our study builds on the prior studies by considering these constructs using a comprehensive conceptual framework by extending the application of signaling theory (ST) in the banking domain and scrutinizing the dual moderating effects of fairness and risk.

This study aims to identify the antecedent factors influencing consumer attitudes and patronage intentions toward an intelligent unmanned convenience store (IUCVS) in Taiwan. The IUCVS is a new smart service that offers customers a novel shopping experience, given that it avoids queues and physical contacts with cashiers. However, studies discussing IUCVS remain scant owing to its brief history.

This research develops a synergistic model combining original unified theory of acceptance and use of technology (UTAUT) constructs with perceived risk and value to test differences between unexperienced and experienced customers’ attitudes and patronage intentions toward IUCVSs. Data collected from 268 experienced and 156 unexperienced consumers were tested against the proposed research model using partial least squares (PLS) structural equation modeling and multi-group analysis (PLS-MGA).

In line with expectations, three UTAUT variables (i.e. performance, effort expectancy and social influence) and perceived value significantly and positively influence consumer attitudes toward IUCVSs. This research confirms the significant and negative direct effect of perceived risk on consumers’ patronage intentions toward IUCVSs. Furthermore, the PLS-MGA results unveil that a significant difference exist in the effects of perceived convenience value on attitudes toward IUCVS between consumers who had experience of using self-service machines and those who have not.

This research successfully fills the research gap by offering a synergistic model for evaluating consumers’ attitudes and patronage intentions toward a new smart service. Several important theoretical and practical implications are provided to help retail managers develop service strategies.

The purpose of this research paper is to analyze the combined effects of driver size and coupling parasitics on crosstalk noise and delay for static and dynamically switching victim line. Furthermore, this paper shows the effect of inductance on delay and qualitatively optimizes its value to obtain minimum delay.

The interwire parasitics are the primary sources of crosstalk or coupled noise that may lead to critical delays/logic malfunctions. This paper is based on simulating a pair of distributed resistance inductance capacitance (RLC) interconnects coupled capacitively and inductively for measurements of crosstalk noise/delay. The combined effects of driver sizing and interwire parasitics on peak overshoot noise/delay are observed through simulation program with integrated circuit emphasis (SPICE) simulations for different switching patterns. Furthermore, the analysis of inductive effect on propagation delay as a function of coupling capacitance is carried out and the optimization of delay is worked out qualitatively. The simulations are carried out at 0.13 μm, 1.5 V technology node.

This paper observes the contradictory effects of coupling parasitics on wire propagation delay; however, the effect on peak noise is of a different kind. Further, this paper shows that the driver size exhibits opposite kind of behavior on propagation delay than peak over shoot noise. It is observed that the delay is affected in presence of inductance; thus, the optimization of delay is carried out.

The effects of driver sizing and interwire parasitics are analyzed through simulations. The optimum value of coupling capacitance for delay is found qualitatively. These findings are important for designing very large scale integration (VLSI) interconnects.

This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables.

To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis.

As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots.

This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers.

This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.