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This research investigates whether and how predecessors’ usernames—as evaluated from a perspective of perceived anonymity—affect successors’ herding momentum through the varying extent of perceived source credibility. Using a unique data set collected from a leading debt-based crowdfunding platform, we classify lenders’ usernames as either anonymous or real-seeming, with the latter referring to usernames that seem to reveal one’s legal name. We find that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility resulting from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. Further, we demonstrate the importance of risk-related factors, in that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period. Our findings provide actionable insights for platform owners to utilize the user heterogeneity with respect to perceived anonymity and hence perceived credibility in herding. These findings are also informative for borrowers who desire to exert effort to encourage participation from the crowd.

In recent years, scholars have explained the factors driving the conversion of residential accommodation to retail units in African cities from an economic standpoint, which indicates high demand for property owners and high potential profit for retail tenants. Nevertheless, these economic perspectives do not elucidate the reasons behind the increasing rate of vacancies in converted retail units amidst the high demand and profit. This paper scrutinizes the rate of vacancies in converted retail units and the behavioural dimensions of conversion and renting decisions in the Greater Accra Metropolitan Area (GAMA) in Ghana. Drawing on the behavioural finance concepts of investor myopia and herding behaviour, findings revealed that the conversion and renting decisions of some property owners and retail tenants in GAMA, respectively, are based on the perceived economic gain realized by other owners and tenants and not informed by locational dynamics and other rudiments of property market analysis. The consequences of these decisions manifest in their negative impact on the exchange and use values of converted retail units. Property owners are compelled to accept lower rents, shorter leases, and external cost of repairs as opportunity costs for the vacant retail units. Retailers, dissatisfied with low level of sales, fold up or continue to relocate from one retail unit to another. The implication of this study, among others, is that the huge capital investment in housing conversion may not be realized unless conversion decisions are based on a critical analysis of the property market.

In the modern world, it is necessary to monitor a user's mobility in everyday life to provide advanced mobile services. Mostly, the location-based services (LBS) depend on both the present and future location of the user, which shows the increasing demand on predicting the user's future location. The prediction of trajectories among two different locations is very important as it aids to optimize travel paths among the locations. Moreover, existing techniques rely on high-quality data to deliver optimal results. In this research, we proposed a new optimized deep learning architecture for mobility tracing. The prediction model is based on Optimized DCNN (Deep Conventional Neural Network) that is already trained with the traced location of the user and from the user’s trained movement the model can predict its next location. For precise prediction, the weight and activation function of DCNN is optimally selected with the aid of HS-EH (Hybrid SeaLion -Elephant Herding) algorithm, which is a hybridization of Sea lion Optimization (SLnO) and Elephant Herding Optimization (EHO). Eventually, the proposed method performance is executed in MATLAB 2019a and compared with state-of-art methods with certain performance metrics. Especially, the accuracy of the proposed DCNN + HS-EH algorithm at training rate 10 is 82.96%, 48.88%, 79.27%, and 61.67% better than existing methods.

The purpose of this research is to identify the factors that support mutual funds investment decisions through overconfidence, risk perception, loss aversion, availability, and herding variables on the investment decisions of the residents of Batam City. Data were collected through questionnaires and processed using multiple linear regression. The results of testing and processing research data show that overconfidence, loss aversion, and herding significantly positively affect investment decisions, while risk perception and availability do not affect investment decisions.

Livestock guarding dogs are increasingly used to reduce carnivore-livestock conflicts, but no information is available to determine any adverse effects that these dogs may have on the health of wild carnivores. We assessed differences in prevalence, richness and severity of parasites in chilla fox (Lycalopex griseus) populations associated with livestock guarding dogs, which were routinely dewormed. We also evaluated whether the parasite assemblages of herding dogs, which were not dewormed, and chilla foxes were more similar in the presence of livestock guarding dogs. Scats of L. griseus and herding dogs were collected and screened for parasite eggs in areas with and without livestock guarding dogs to determine differences in prevalence, richness, intensity and parasite aggregation. We did not find any association between livestock guarding dogs and differences in parasite richness or prevalence of parasites. The intensity of parasites was higher among foxes positive to parasites when LGDs were present. A lower proportion of foxes with low parasite burdens occurred in areas with LGDs compared to areas without LGDs, but this difference was not significant at the population level. Our findings show the need to continue studying the effects of livestock guarding dogs on the health of wildlife with larger sample sizes and more locations.

PurposeThe purpose of this paper is to explore how heuristics are formed and whether herding and prospect theory act as antecedents to heuristics. The relationship is explored specifically for millennials.Design/methodology/approachThe proposed relationship is explored specifically for millennials. Herding and prospect theory are modelled as antecedents to heuristics. The study uses survey data from 923 millennials from India to test the model for two financial products: equity and mutual funds. Regression analysis is used to evaluate the model.FindingsFindings support the role of herding and prospect theory as antecedents to heuristics of millennials although to varying degrees for equity and mutual fund investments. The impact of herding on heuristics is likely to be smaller for equity investments as compared to mutual fund investments.Research limitations/implicationsThe findings provide insights into how heuristics are formed for millennials. The findings add to literature by beginning a new line of inquiry on how heuristics are formed. Since the model is tested on a single generation, future research can test the model on other generations. In addition, future research can also add more antecedents to our proposed model.Practical implicationsFindings from this study can provide financial planners and marketers with an understanding of how heuristics are formed for millennials. Financial planners can use these insights while providing financial advice to this generation and marketers can use them to create more relevant outreach.Social implicationsFinancial investments are an important conduit for financial security. By understanding the cognitive processes that influence financial investment decision-making, it is possible for educators to create content appropriately and for financial planners to advise clients accordingly to enable optimal financial decisions that will be wealth-creating.Originality/valueExisting literature primarily treats heuristics, herding and prospect theory as being independent of each other. The authors take a novel approach to model the antecedents to heuristics to be herding and prospect theory. The model is tested on millennials for two financial products: equity and mutual funds.

PurposeThe study aims to investigate the barriers in implementing social distancing at the workplace as an aftermath of the COVID-19 pandemic period.Design/methodology/approachStudy design consists of a review of literature, data collection and analysis. It encompasses identification, verification and analysis of the relationships among the barriers. Data have been collected from a panel of experts on matrix-type questionnaires from workplaces. Interpretive structural modeling (ISM) augmented with “Matrice d' Impacts Croise's Multiplication Appliquée a UN Classement (Cross Impact Matrix Multiplication Applied to Classification)” (MICMAC) for structural analysis.FindingsThe literature has identified twenty major barriers to implementing social distancing at the workplace. The research findings reveal/show that the barrier “matter of disrespect” occupies the bottom level in the ISM model. Therefore, it is the most critical barrier; whereas, employees with disabilities and “herding culture” are crucial as they occupy the next lowest level, therefore, are crucial. Moreover, there are ten barriers positioned in the middle of the model having moderate-severe effects, and seven falls on the top level of the model having relatively less severe effects. Results of MICMAC affirm and avow the results of ISM.Research limitations/implicationsThe study will have profound theoretical and practical implications for stakeholders since it provides lot of new useful and valuable information, gives relational insights and determines priorities subject to usual limitations of survey research.Originality/valueIt is an original attempt to make some sense of practicability of social distancing for stakeholders including policymakers, frontline health workers and public at large.

I conduct an experimental investigation of observational (social) learning in a simple two-armed bandit framework where the models are based on Bayesian reasoning and non-Bayesian count heuristics providing different predictions. The agents can choose between two alternatives with different probabilities of providing a reward. They must make their choice in order to see the outcome and act in a sequence. They can base their decision on the choices of the predecessors and the outcomes of their own choice. The results of the experiment follow neither Bayesian Nash Equilibrium nor Naive herding model (BRTNI): Subjects follow and cascade on choices that contain no information about the state of the world, and, therefore, sustain losses when learning from others. I also test the Quantal response equilibrium and the robustness of this theory.

The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

This paper uses biographical information of executives and directors of REITs in the US to show whether, and through what channels, top executives of REITs are influenced by their social peers when determining capital structure risk control strategies, especially in critical periods. Our focus is on the period of the 2007–2009 Financial Crisis. We find that peer influence through past employment and sharing activities significantly facilitate peer learning in making decisions on debt maturity extension, but does not affect leverage reduction. We find that being educated from the same school carries some effects on leverage reduction, possibly via its influence on managers’ personal traits. However, concurrent employment does not play a role in determining either of the strategies. We further verify the existence of influence of social network in decision making of REITs in 2015 in preparation for a boom at the beginning of the up-market. Hence, our study highlights the strength of peer connections in clarifying possible sources of herding in REITs decisions.

Shifting human–wildlife conflict towards coexistence requires a robust understanding of where conflict happens and why. Spatial models of livestock depredation by wild predators commonly identify depredation hotspots in areas where livestock are most abundant (e.g. nearer villages or pasture). This may reflect underlying livestock distribution, rather than imply these areas are inherently risky for livestock. This limits the predictive power of these models and their usefulness for conflict mitigation and wild carnivore conservation. Here, we build spatial models of both cattle depredation (530 attacks mostly by lions and hyenas; 2009-2013), and cattle presence (14 GPS-collared herds; 2010-2012) near Hwange National Park, Zimbabwe. We use Bayes’ theorem to combine the cattle depredation and presence models to quantify risk as the conditional probability of depredation given livestock presence. Our raw depredation models predicted higher depredation rates where cattle presence was more likely (near villages and in more open habitats). By contrast, our risk model predicted higher risk further from human activity and in more dense vegetation (where depredation rates were higher than expected given the low probability of cattle presence). Risk has also increased sharply towards protected areas (core carnivore habitat). Our formulation of risk captures high-risk areas as those where livestock are most accessible (i.e. vulnerable) to predators as opposed to simply where they are most available (as in much previous work). We make recommendations for livestock protection and wild carnivore conservation based on our quantification of risk, such as where to avoid herding livestock and which areas to prioritize for livestock protection. Our approach may be profitably applied to guide safer livestock grazing or herding in other contexts where depredation and livestock movement data are available. We hope that the concepts and methods that we develop here will help advance the future study and mitigation of human–wildlife conflict more generally.

Traditional herding is an ancient activity that has continued from the beginning of human life on earth until today. Over time, alternative methods have replaced traditional livestock management methods, but in many countries, traditional herding still has a large share in meat and dairy production. Therefore, to improve the efficiency of this system, analysis of energy indicators is important. In this study, the common livestock system in northeastern Iran (Kalat region) and energy efficiency and productivity were evaluated. For this purpose, the identification of important livestock centers and the characteristics of all rangeland in the region were prepared and with a comprehensive and accurate evaluation, the target herd was determined. It monitored all livestock activities over five years, and it summed the input and output energies using the MAFF equation and Nicol coefficients. It also estimated the volume and price of livestock products to obtain real income. By summarizing all the information, energy efficiency and productivity indices were estimated annually. So, it estimated the energy efficiency index in the traditional herding of Kalat at 31% according to the sheep breed (Kurdikhorasani) of the region. The energy productivity index also recorded an almost downward trend during the study period and showed that it fluctuated entirely based on changes in real income. The results showed that energy indices are relatively lower than in other regions, and there is a need to reform the prices of livestock products and improve management methods for the sustainability of traditional herding.

In India, the coronavirus (COVID-19) pandemic-induced country-wide regulatory lockdown and consequential supply-chain disruptions and market instability have all posed serious challenges before the regulators and policymakers. Amid the pandemic, the stock market showed return volatilities primarily due to the unexpected investors’ behaviour. One of the behavioural biases is herding, which has the power to wreck the market equilibrium and shatter the market efficiency. Given that the pandemic has generated unprecedented spirals of uncertainties across the globe, thereby creating interruptions in the pattern of stock market investment decisions, this study examined the herding behaviour of 54 stocks of banking and financial services sectors listed in the national stock exchange. In the quantile regression framework, the study provides evidence of the presence of herding for public sector banking and financial services under the bull market conditions during the pandemic in the 90th quantile of the return distribution. This finding has implications for the mispricing of financial assets in these sectors. So, the study suggests removing information asymmetry among the market participants and devising policy initiatives for ensuring market stability.

Tradable green certificate (TGC) scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment. TGC market efficiency is reflected in stimulating renewable energy investment, but may be reduced by the herding behavior of market players. This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents, communication structure, and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency. The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency, especially when the borrowing is allowed. In addition, the fundamental strategy is diffused by herding evolution, but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism. Moreover, the herding behavior may evolve to an equilibrium where the revenue of market players is comparable, thus the fairness in TGC market is improved.

The herd behavior of technology users, i.e., adopters observe the decisions (but not the reasoning) of prior adopters and imitate their system usage behaviors, is prominentin the information technology (IT) industry. However, such en mass adoptions areprone to be fragile. Adopters may reverse their decisions, after an initial adoption, andabandon system use when updated contradictory information is presented. This phenomenon of herd-like abandonment, at both individual and organizational levels, is significant and requires more investigation, since it is connected to the durability of particular products and technologies in the marketplace. In addition, IS behaviors of users at the later phases of IS life cycle, i.e., termination phase, are the primary source of benefit for organizations. This study develops and empirically validates a theoretical model of IS abandonment in a herding context. We test our model via a longitudinal research design, which surveys adopters at two different points in time. The study examines the determinants of adopters’ abandonment intentions, which occure specially after an initial en mass adoption (i.e., a herding setting). Results suggest that post-adoptive task-technology fit, perceptions of niche, and observation of a criticalmass of abandoners are all salient factors affecting IS abandonment intentions.

Reindeer herding involves hard physical work carried out in a cold climate under variable weather conditions. In the fall and winter, herders’ work in northern Finland includes collecting and moving reindeer herds to round-up sites, working in round-ups, slaughtering and processing meat as well as daily feeding and monitoring of the animals in the field. To study the experiences and perceptions of coping with cold among physically active herders in harsh winter conditions, we interviewed 22 herders from six herding districts of the central reindeer management area within the north boreal coniferous forest zone. We focused on behavioral and cultural strategies that accompany the physiological cold adaptations. Semi-structured interviews revealed that the main behavioral and cultural strategies used by herders to successfully carry out their duties while avoiding cold-related injury include clothing, physical activity, nutrition, and shelter as well as protecting vehicles and devices. Herders across sex, age, and herding district reported using modern layered clothing developed for extreme conditions, often combined with traditional footwear and clothes made of reindeer fur or woollen fabric. In addition, herders increase their physical activity; eat warm, energy-rich foods; make fires; stay overnight or take breaks in a house or a cabin, a car, or other protected places to reduce exposure to the harsh environment. Coping with extreme conditions not only requires flexibility, preparedness, and innovation from the herders but also thoughtful caution when approaching and managing unexpected situations. We conclude that modernization of reindeer husbandry, climate change, and rapidly increasing land use competition not only drive herders to modify their behavioral and cultural coping mechanisms for extreme weather conditions but may also create new, unexpected vulnerabilities.

Geopolitical tensions between nations play a crucial role in triggering volatility and affecting the investors’ behavior in stock markets. This empirical work attempts to detect the traces of herding and bubble embedded in the Indian stock indices of CNX Nifty 50 and CNX Nifty 100 (both in High-Frequency Trading domains) during the latest events of geopolitical tensions escalated between India-China and India-Pakistan. An event window approach is employed to capture the impact of these events on herding behavior and information uncertainty in the considered stock indices. Multifractal Detrended Fluctuation Analysis (MFDFA) is applied to compute the Hurst value in all the trading days of the event window. The results of both indices exhibit conclusive evidence of herding and bubble formation during the overall period of geopolitical tensions between India-China and India-Pakistan. However, the degree of herding in the stock indices intensifies to a profound pattern when the tensions between India and China escalated into deadly violent clashes, and also during the heightened tensions between India and Pakistan that eventually ended up in airstrikes across the boundaries. The overall level of information uncertainty depicted by entropy is within control. The volatility in these stock indices has been confirmed to follow a unidirectional pattern. AcknowledgementsThe authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Professor, Department of Finance and Analytics, RV Institute of Management, Bangalore, India) for his instrumental role in encouraging and motivating them to accomplish this research task. The authors also extend their sincere thanks to Dr. Manu K.S. (Assistant Professor, School of business and management, CHRIST (Deemed to be university), Bangalore, India) for his continued support throughout this empirical investigation.

This article examines interspecies cooperation in camel herding in Somaliland. It presents the case of a particular joint activity in this task-scape: moving a camel herd, by leading and driving it, from the night-camp to the daytime grazing area and back. The analytical aim is to clarify the role that skills and nonhuman agency play in the constitution of cooperative human-camel relationships. On the basis of empirical data, collected in a multispecies ethnographic project by following and observing one herd and herder closely, this article demonstrates how nonhuman agency, as an individual capacity to engage in an activity and an epistemological potential, manifests in this human-camel cooperative task. Cooperation is made possible through human-camel sociality and intersubjectivity, through the ability to interpret and respond to each other, and it depends on the empathetic acknowledgement of the enabling or disabling powers of each counterpart, her or his agency. Leading and driving camels is a skilled practice requiring the responding and enabling capacities of the cooperation partner. Hence, it is a case of distributed skills – distributed in the sense that skills of humans and nonhumans are intertwined in this practice, that they complement each other.

This paper investigates hedge fund herding at the industry level and its impact on industry returns. Although the level of industry herding on average is substantially weaker for hedge funds compared to non-hedge fund institutions, we find that industries that experience heavy herding by hedge funds experience return reversals in the long-run. We also provide evidence that non-hedge funds follow especially hedge funds’ sell herding industries in the subsequent quarters. We conclude that the long-run return reversals observed in hedge fund sell-herding industries are due to non-hedge funds’ failure to react to positive news in subsequent quarters in a timely manner.

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence (SI) approaches, Swarm Robotics (SRs) became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown environment is addressed. Our main objective is to propose a Robotic target search strategy based on the EHO (Elephants Herding Optimization) algorithm, namely Robotic-EHO (REHO). The main additions were the collision-free path planning strategy, the velocity limitation, and the extension to the multi-target version in discrete environments. The proposed method has been the subject of many experiments, emulating the search of infected individuals by COVID-19 in a context of containment within complex and unknown random environments, as well as in the real case study of USA. The particularity of these environments is their increasing targets' number and the dynamic Containment Rate (CR) that we propose. The experimental results show that REHO reacts much better in high Containment Rate, early start search mission, and where the robots' speed is higher than the virus spread speed.

The use of intelligent college English writing training system will certainly promote the traditional teaching structure and realize a new and efficient English writing teaching mode. On the basis of machine learning and the herd effect algorithm, this article constructs an artificial intelligence-based English intelligent writing system. Moreover, in view of the shortcomings of traditional models and the characteristics of intelligent English writing, this paper proposes an improved algorithm for optimization of swarm particle walking paths. In addition, this article proposes a relative attractiveness to initialize the formation of small-scale groups based on the herd effect. Then, in the process of intelligent writing, by establishing an information sharing mechanism between groups, each group is continuously updated and reorganized according to the relative attractiveness of the group, so that the writing process can be simulated more realistically. From the experimental research, it can be seen that the model constructed in this paper has a certain degree of intelligence.

Background: Glaucoma and diabetic retinopathy are known to be the prime reasons for causing irrevocable blindness in the world. However, the complete vision loss can be obstructed through regular screening of the eye to detect the disorder at an early stage. Objective: In this paper, we have presented a novel nonlinear optimization technique that helps in automatic detection of the optic disc boundary from retinal fundus images thereby satisfying the anatomical constraints by using elephant herding optimization algorithm. Methods: In our approach, median filter has been used for the noise removal in retinal images. The pre-processed image is passed to the metaheuristic algorithm known as Elephant Herding Optimization algorithm. Results: The proposed technique for optic disc segmentation has been applied and tested on four standard publicly available datasets namely DRIVE, DIARETDB1, STARE and DRIONS-DB. The ground truth of the optic disc boundary has been collected from two experts of glaucoma: Expert A and Expert B who are specialists of glaucoma. The quantitative and qualitative analysis has been done to evaluate the performance of optic disc segmentation techniques. Conclusion: The proposed technique for optic disc detection helps to obtain the smooth boundaries in retinal fundus images. The aim has been successfully achieved by proposing an approach using EHO to achieve optimized solution. The effectiveness of the approach has been evaluated on for benchmark datasets and the acquired results have shown the accuracy values to be 100% for DRIVE, 100% for DIARETDB1, 99.25% for STARE and 99.99% for DRIONS-DB.

Context Herding and mustering procedures during livestock management can be time-consuming, labour intensive, and costly. The ability to gather animals virtually is an enticing notion but technology to do this is not widely commercially available. Aims The eShepherd® virtual fencing system being developed for cattle may be able to remotely herd animals. This system operates via global positioning system, and requires animals to wear a neckband device. Animals are trained to associate an audio tone with an electrical pulse to avoid a virtual boundary. Methods Experiments were conducted with cattle using pre-commercial prototypes of the automated virtual fencing neckbands, and with sheep using manually operated dog training collars implementing the same virtual fencing algorithm to explore the potential of this technology for herding, and optimal fence designs for herding success. In the first experiment, five groups of 12 cattle were moved down a 344 m paddock using three different fence placement designs. Results The most successful design for cattle herding was a back fence that followed behind the animals to prevent them from turning back in the wrong direction. The fences were manually activated by personnel based on the cattle movement. The same type of fence design was manually applied to two groups of six sheep to successfully herd them down a 140 m paddock in the second experiment. Conclusions All herding was highly dependent on the animal’s own pace of movement as no signals were applied to ‘push’ the animals, the systems only prevented movement back in the wrong direction. The pre-commercial prototype of the automated eShepherd® device used is now obsolete and testing with updated versions would be needed to confirm its application for animal herding. Implications These preliminary trials indicate potential for virtual fencing technology to herd livestock, but technology improvements are required, and an automated device for sheep is not yet available.

PurposeWith the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets.Design/methodology/approachThe study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.’s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases.FindingsThe descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile (t) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding.Practical implicationsDrawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system.Originality/valueThe present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016–2017) and the outbreak of coronavirus (early 2020).

We propose a solution to the problem of herding by caging: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we guide the sheep to a target location without letting them escape from the herders along the way. We model the interaction between the herders and the sheep by defining virtual “repulsive forces” pushing the sheep away from the herders. This enables the herders to partially control the motion of the sheep. We formalize this behavior topologically by applying the notion of caging, a concept used in robotic manipulation. We demonstrate that our approach is provably correct in the sense that the sheep cannot escape from the robots under our assumed motion model. We propose an RRT-based path planning algorithm for herding by caging, demonstrate its probabilistic completeness, and evaluate it in simulations as well as on a group of real mobile robots.

Purpose In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods. Design/methodology/approach This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem. Findings In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services. Originality/value The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

This paper investigates the impact of oil price changes on herding behaviour in the Gulf Cooperation Council (GCC) stock market. For this purpose, a sectoral analysis was employed. Based on a combination of the methodologies of Ulussever and Demirer (Cent Bank Rev 17:77–89, 2017) and Medhioub and Chaffai (Int J Financ Stud 7(65):1–11, 2019) and using daily prices for the five GCC sectors from 6 July 2014 to 31 December 2019, the results showed that the impact of changes in oil price movements on herding behaviour differs among sectors and subperiods. Further, we discovered that some sectors herd around the oil price returns during falling and that oil return dispersions have a dominant influence on some sectors during downward and/or upward periods.

Multiple-input multiple-output (MIMO) systems invite many researchers to contribute more in channels selection for improving the performance for enhancing the throughput and quality of the received signal. It is important to notice that target channel selection will also improve the performance while decreasing the signal-interference-plus-noise ratio. In MIMO channel allocation, it is found that utilization of resources tends to be a dangerous issue which could be tackled by identifying secondary users in addition to primary user for disseminating the information. This paper focuses on allocating the dynamic channels effectively by applying a centralized heuristic algorithm which is referred as cuckoo search with improved elephant herding optimization. In the previous approach, poor resource allocation causes desperate issue in MIMO channel allocation while calculating the energy of each user available in the network. Substance near multicast protocol has been applied for optimizing the energy along with elephant herding optimization algorithm in order to allocate channels dynamically for global optimization process. An improved elephant herding optimization algorithm for multiuser MIMO framework is integrated in the overall remote region. This initiative signifies the greatest number of mutually legitimate clients and the feasible bit rates of clients in the overall remote region. The iEHO algorithm concurrently enhances energy proficiency and framework throughput by client assortment and power distribution. Our trial outcomes ascertained that the recommended method of working fundamentally excelled the essential EHO. Simulation has been done by using MATLAB which is assumed to be the best simulator to show the results without missing the clarity.

We test the impact of herding behaviour on the risk pricing in the Egyptian Stock Exchange (EGX) by adding an additional risk factor reflecting herding behaviour to the Fama and French three-factor model. We construct a portfolio to mimic an additional risk factor related to herding behaviour, in addition to the original risk factors in the Fama and French three-factor model. The three-factor model will be tested in its original form and re-tested after adding the herding behaviour factor. The study is based on Hwang and Salmon methodology, in which the state space approach based on Kaman’s filter was used to measure herding behaviour. We used monthly excess stock returns of 50 stocks listed on the EGX from January 2014 to December 2018. The results do not support Fama and French model before and after adding the herding behaviour factor, therefore, there is no effect of herding behaviour on the risk pricing in the Egyptian Stock Exchange.

This paper investigates the dynamic and differential effects of prefunding on reward-based crowdfunding markets plagued by information asymmetry. Prefunding, an innovative feature in crowdfunding, enables founders to share project information with potential backers before fundraising begins. By collecting and analyzing daily project-level panel data from one of the world’s largest crowdfunding platforms, we found that projects with the prefunding feature were more likely to succeed in reaching their funding goals and the effects of prefunding on the amount of funds raised remained positive and significant over time. In probing why this occurred, we used text analyses and revealed that the mechanism driving the funding premium was the specific types of prefunding information shared between founders and potential backers (volume, length, and sentiment). Further, in examining the sources of funds, we found that prefunding information first attracts funding from regular backers, followed by lottery backers. This herding behavior creates two intertwined funding streams—a primary and a secondary—for prefunding projects. Finally, using counterfactual decomposition analysis, we identified the types of projects that benefited the most from prefunding and found that prefunding democratizes funding outcomes. These findings and insights into information sharing, herding, and differential effects of prefunding contribute to the OM-IS research on operational designs of reward-based crowdfunding platforms that serve early-stage ventures in online environments with minimal informational oversight and regulations.

At recent times, customer churn is an important activity in quickly developing industries such as telecom, banking, e-commerce, etc. Earlier studies revealed that the cost of getting a new customer is considerably higher than the cost of retaining the existing ones. Therefore, it becomes essential to predict the nature of customer churn for retaining the customers to a greater extent. The advent of deep learning (DL) models have begun to be employed for efficient CCP. This paper presents a new Adagrad Optimizer with Elephant Herding Optimization (EHO) based Hyper-parameter Tuned Bidirectional Long Short Term Memory (AG-EHO-BiLSTM) for CCP in Internet of Things (IoT) enabled Cloud Environment. The proposed AG-EHO-BiLSTM model initially acquires the customer data using its devices like smart phones, laptop, smart watch, etc. Next, the gathered data will be classified by the use of Bi-LSTM model, which determines the customers as churner or non-churner. The efficiency of the Bi-LSTM model can be increased through hyper parameter tuning techniques, namely Adagrad optimizer and EHO algorithm to optimally select the parameter values namely learning rate, number of hidden layer and epochs. The performance validation of the AG-EHO-BiLSTM model takes place on benchmark dataset and the simulation outcome reported the supremacy of the AG-EHO-BiLSTM model over the comparative methods.

As the world embraces the evolution of cognitive cyber–physical systems, there is an emergence of systems with the ability to comprehend and act on information efficiently while also supporting human operators to achieve ethically aligned mission objectives consistently. This human‐autonomy teaming (HAT) relationship will evolve with technology and increasing levels of autonomy. It is essential to identify the role of trust and systematically develop to that role to minimize challenges in HAT relationships. Life‐learning processes are needed to guide system developers to assess meaningful interactions and to refine the critical contexts that require trust continuously. By assessing the presence of trust, cognitive competence, and meaningful interactions, human factors specialists and human systems engineers will also be able to assess the sociotechnical system evolution. This chapter first defines trust, both as a concept and for use in HAT. It then proposes processes for how to assess trust, especially to meet recently proposed IEEE standards and United Nations articles concerning meaningful human control. The chapter then examines how our research program on the herding of sheep using unmanned aerial vehicles (UAVs) is validating HAT concepts and the proposed processes. The program is concerned with teaming farmers with smart autonomous capabilities to assure meaningful farmers' control. Meaningfulness needs to account for public obligations and ethical responsibilities. This application area potentially solves extant ethical challenges in shepherding in Australia concerning the use of dogs and timely monitoring and action in vast sheep stations while also allowing the investigation of ethical challenges around continually evolving artificial intelligence in the system. Ethically designed HAT systems are about pragmatic trade‐offs with prior processes, yet in this future context of life learning of intelligent systems, can now be revised through‐life with changing expectations and improvements.

The semi-arid Lokere and Lokok catchments in northeastern Uganda are experiencing land use and land cover (LULC) change driven by policies and actions aimed at pastoralist sedentarisation. While these efforts present a trajectory of a landscape dominated by farming, livestock herding or grazing persists. The objective of this study was to project medium, and long-term LULC for Lokere and Lokok catchments in Karamoja, Uganda. We applied automatic multi-perceptron neural network, built on Markov chain modeling method, along with multi-criteria evaluation strategies; all embedded in the IDRISI Land Change Modeler (LCM) to project the catchments’ LULC to the year 2030 and 2050. The model was trained using 1994 and 2003 LULC, and validated with 2013 LULC. Results of three modelled policy scenarios; business as usual (BAU), pro-livestock and pro-farming; to the years 2030 and 2050 showed that small scale farming (SSF) would increase in all scenarios, even if policy shifts to promote livestock rearing. Pro-farming policies would, in both 2030 and 2050, result in reduction of grassland as SSF increases; doubling the 2003 land area by 2050. The results of this study facilitate assessment of potential impacts of the future LULC and policy evaluation in the catchments.

In a digital, global world, a paradigm shift in marketing has led companies and customers to become increasingly interconnected through the Internet and social media. Drawing on online review data ...

The ubiquity of information, in part a consequence of the fast growth of Internet technologies, provides individuals with previously unknown opportunities to acquire and share information about new technology products. We witness numerous situations where potential adopters observe the decisions (but not the reasoning) of others and imitate their adoption behaviors. This implies that herd behavior can lead to en mass adoption. However, individuals possessing different characteristics exhibit different degrees of herd behavior. The characteristics of a given technology must also fit with the requirements of the task(s) that a technology adopter wants to fulfill. It is important to understand these phenomena in the herding context because they can influence a technology's staying power. We examine how several different user and technology characteristics interact with the antecedents of herd behavior, observed popularity of prior adoption and perceived uncertainty, to predict one's individual propensity for imitation in technology adoption. We test our research model via an online experiment with 260 respondents. One key finding is that while prior research has not found uncertainty to have the expected effect on propensity to imitate, it does in fact have a significant effect when various individual difference and technology moderators are taken into account.

This study mainly aims to study of Social and Physical Features of Job Mobility from herding and farming to tourism of the Bedouin oasis of Siwa in Matrouh governorate by identifying the indicators and type of professional mobility of the researchers in the Bedouin community of professions (grazing, agriculture, tourism), and identifying and proposing the most important problems facing those working in Siwa and their proposal to solve them. The research was based on the method of social survey in the sample, were selected 100 research, the research was based on the method of social survey in the sample. And results showed: 1- That the economic process is a major driver of professional mobility in Bedouin society with an average of )76.08( in favor of the tourism. 2- The results showed that there is a rise in professional mobility among the researchers and by (33%) Of the total sample for the tourism profession. 3- It is clear from the results that the profession has an important role in the event the social status of people, the total with a weighted average of percent (78.10) in favor of the tourism profession. 4- The results of the study showed that education is a major driver of professional mobility in Bedouin society with a weighted average of percent (62.33) in favor of the tourism profession. The study recommends increasing the work of development projects in the study area in the field of tourism, so the study recommends attention to education in the Bedouin community in various types to create a generation of multi-talented and skills needed by the workforce in the desert community.

PurposeThe present study looks into the mimicking behaviour in both normal and asymmetric scenarios. It, then, considers the contagion between the USA and the BRICS stock markets. Finally, it examines herd behaviour in the wake of a major banking policy change concerning the bloc under study.Design/methodology/approachThe current empirical analysis employs daily, weekly and monthly data points to estimate relevant herding parameters. Quantile regression specifications of Chang et al. (2000)'s dispersion method have been applied to detect herd activity. Also, dummy regression specifications have been used to examine the impact of various crises and strategically crucial events on the propensity to herd in the BRICS markets. The time period under consideration ranges from January 2011 until May 2019.FindingsThe relevant herding coefficients turn insignificant in most cases for normal and asymmetric scenarios except for China and South Africa. This can be traced to the anti-herding behaviour of investors, where individuals tend to diverge from the consensus. However, turbulence makes all stock markets to show some collective trading except Russia. Further, the Chinese stock market seems immune to the frictions in the US stock market. Finally, the Indian and South African markets witness significant herding during the formation of a common depository institution.Practical implicationsMost stock markets seem to herd during turbulence. This revelation is of strategic importance to the regulators and capital market managers. They have to be cautious during crises periods as the illusion of being secured with the masses ends up creating unprecedented frictions in the financial markets.Originality/valueThe present study seems to be the very first attempt to test the relevant distributions' tails for convergent behaviour in the BRICS markets.

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

This study empirically examines herd behavior for fast moving consumer goods (FMCG) sector stocks under varied market return conditions and the period during the global financial crisis and its aftermath. We examine the sample of stocks trading on the Nifty FMCG Index of the Indian equity market from January 2008 up to December 2018 using the dispersion measure of cross sectional absolute deviation and examine its relationship with the market return to explore herd phenomenon. Quantile regression estimate is used and the results of the study validate rational asset pricing models as the sector does not display herding. In contrast, anti-herd behavior at lower and median quantile values is observed. A possible reason can be the non-cyclical nature of the industry where investors rely more on the fundamentals rather than crowd chasing. We also findthe absence of herd phenomenon during the market asymmetries of bull and bear phases, extreme movements, the period of the global financial crisis, and afterward. We further examine herding under the impact of the information technology (IT) industry and conclude that significant return movements in IT sector impact dispersions in the FMCG industry. Also, there is a co-varying risk between the two sectors confirming the spillover in an integrated market.

L’élevage pastoral au Sahel est extrêmement dépendant des irrégularités climatiques, et particulièrement de la qualité de la saison des pluies qui conditionne la repousse des pâturages et le remplissage des points d’eau. Les éleveurs nomades et transhumants adaptent leurs stratégies de déplacement à l’état et à la disponibilité de ces ressources. Les outils de suivi de la sécurité alimentaire de ces territoires pastoraux doivent donc intégrer, en plus des données spatialisées sur la biomasse, des informations sur ces mouvements pastoraux et sur les facteurs qui les conditionnent. Pour accompagner les systèmes d’alerte locaux au Sahel, l’organisation non gouvernementale Action contre la Faim propose un modèle de vulnérabilité pastorale basé sur des données satellitaires acquises en temps réel et sur la connaissance des pratiques pastorales. Ce modèle permet d’alimenter en informations un système d’alerte précoce en identifiant les zones d’intérêt pastoral déficitaires en ressources plusieurs mois avant la période de soudure potentiellement problématique. La confrontation avec l’information concomitante collectée sur le terrain par des relais sentinelles permet une surveillance de la situation pastorale, contribuant au développement des infrastructures ou au déploiement de l’aide humanitaire.

Climate change is affecting virtually all environments in the world today. The Central Asian mountains, where livestock herding is the main source of livelihood, are among the environments predicted to be most affected. In this paper, we use meteorological records and herder perceptions to improve the understanding of climate change and examine how different climate change scenarios will affect herder livelihoods in the Tost-Tosonbumba Nature Reserve of southern Mongolia. Herders with generation-long herding experience perceived that there had been changes in all eight meteorological variables examined in this study between 1995 and 2015, including winter temperature, summer temperature, summer precipitation, frequency of intense rain, frequency of drizzle rain, wind speed, number of windy days, and snow cover. Herder perceptions and meteorological data showed the same patterns for 3 of the 8 variables at α = 0.05 and for 5 of the 8 variables at α = 0.10. Herders also predicted that all 9 climate change scenarios presented would have negative impacts on their practices and livelihoods. Our work suggests that herder perceptions of climate change can provide important information on changes in the climate and insights on factors that put their livelihoods at risk.

PurposeThis study aims to investigate the determinants of herd behavior in online hotel service evaluations, focusing on the cultural and geographic distance characteristics of customers.Design/methodology/approachOn the basis of 381,462 TripAdvisor reviews of hotels in the USA written by more than 100,000 customers from 92 countries, this study uses the empirical analysis to explore the collective roles of cultural distance, geographic distance and hospitality experience on herd behavior in online hotel ratings.FindingsCultural and geographic distances between customers and product and service locations positively affect herding and these two effects are substitutable. The hospitality experience of customers attenuates the impacts of distances on herding. These results are robust for multiple hotel service ratings.Practical implicationsFindings help hotels understand perceptual biases of customers on hotel services under the social influence and consequently develop effective marketing strategies to boost hotel revenues and increase profitability.Originality/valueThe research contributes to hospitality and online review literature by understanding how cultural and geographic distances shape online hotel service evaluations under the root of the uncertainty of decision-making and the observation of others’ behavior. The research also contributes to the distances in international business literature by deepening the understanding of the substitution and heterogeneity of distance effects. Methodologically, a time-varying and monotonously increasing variable is constructed to depict customers’ hospitality experience. The extensive data volume ensures the generalizability of our results.

A significant reason for the concentration of demand in a subset of the supply in the peer-to-peer market for tourist accommodation is herding behavior, by which the decisions of the first guests are imitated by those who follow. This article proposes a profit- and utility-maximization microeconomic model and implements it with data of Airbnb listings corresponding to 10 European cities. Results show that the influence of each additional review is positive but decreasing, inducing a more balanced distribution of demand among offered accommodation and thus dampening the herding effect. Moreover, reservation policy—specifically, enabling the instant booking option—is a key to explain the initial push that accommodations need to be demanded now and, hence, to increase their possibilities of being demanded in the future.

Devoted to Amarsana Ulzytuev, one of the most fascinating contemporary poets, A. Uvitsky’s article examines his work in the contexts of modern Russian and the little researched Buryat poetry and looks at the results of his pet project – to establish anaphora as the regular accentual rhyme for Russian poetry. The author admits that Ulzytuev’s idea to introduce the regular anaphoric sound to Russian poetry, as detailed in his books Anaphoras [Anafory] (2013) and New Anaphoras [Novye anafory] (2016), is theoretically viable. But more than its ongoing implementation, he is interested in Ulzytuev’s creative evolution: the poet’s oeuvre is typified by a natural combination of two poetic cultures – one Russo-European and the other Buryat- Mongolian, according to P. Basinsky. In Uvitsky’s view, the archaic manner of the Buryat-Mongolian poetics, realized through the use of anaphora, among other things, is not inconsistent with the deep and sophisticated European poetics, with an epic meaning expanded and enhanced by the poetic sound championed by Ulzytuev.

The transition from hunter-gathering to food-producing societies in the Mediterranean zone of north Africa was complex and variable, likely influenced by local ecological conditions as well as the socio-economic origins of the population. The adoption of domestic plants and animals was piecemeal, with hunting and gathering continuing as an important part of local subsistence strategies. Here, we investigate the timing and extent of the adoption of agricultural practices, namely herding and cultivation, in three diverse coastal and inland Early Neolithic sites in the Mediterranean Maghreb region, namely Ifri Oudadane, Ifri n’Etsedda and Hassi Ouenzga. Results from absorbed lipid residues extracted from 306 potsherds from these sites are correlated with information from faunal and archaeobotanical assemblages. Our findings suggest that agricultural practices, regarded as being of Neolithic origin, were never fully adopted in the Maghreb but rather that these farmer/foragers adopted a range of strategies including low-level food production (exploiting cereals and animal products, including meat and milk), gathering of wild plants and marine shellfish, and hunting both small and large sized game. These broad-spectrum farmer/foragers were clearly both flexible and resourceful and likely adapted their subsistence practices to maximise resource availability in an increasingly unpredictable environment.

This paper investigates expert bidding imitation in peer‐to‐peer lending platforms. We employ data from Renrendai.com, which contains information of about 170,000 investors who placed almost four million bids on 111,234 loan listings from 2010 to 2018. The experts are defined as investors who either have more central roles or who spend more time or money on the network. We find that an average investor mimics the bids of expert lenders. Inactive lenders learn top investors’ lending behaviour through observational learning and then, follow their actions, although they do not know the experts’ identity. Finally, we show that experts rarely imitate other experts, yet they exhibit herding behaviour.

Herding can be characterized as a coordination game with two strategies for minimising risk: increase herd size (livestock quantity) or increase livestock body mass (livestock quality). In this paper I demonstrate that the selection of herd maximisation as a risk management strategy in the Northern parts of Norway has been influenced by a history of intra-group competition exacerbating herder conflict and lack of trust. In the South herder-farmer conflicts have increased herder coordination and trust, resulting in the selection of increasing livestock quality as the dominant risk management strategy.