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This study aims to examine the effect on stock returns of 28 terrorist and military events occurring between 1963 and 2012. The authors divide the sample and examine these attacks on the basis of industry, country targeted, location, terrorism versus militarism and predicted overall impact.

The authors measure the effects of the events in our sample along several dimensions: in the aggregate; comparatively across industries; by each event’s predicted level of impact; by the type of event (terrorist versus military); by the location of the attack (USA or outside the USA); and by whether the USA was, directly or by proxy, the primary target of the attack.

Stock returns are significantly lower for those industries predicted to be most hurt than for other industries. Events that the authors predict to be of high impact to the market are followed by significantly lower returns than events we predict to be of low impact. Stocks perform significantly worse on the days of terrorist events than on the days of military events, but the opposite is true for the day after. Significantly lower returns follow events that occur inside the USA or where the USA was the primary target.

This study focuses on 28 high-profile events over a 50-year period and makes several new contributions to the literature. The authors find compelling cross-sectional differences between stock returns at the industry level as well as predictable differences in mean returns between events. The authors distinguish between terrorist and military attacks and also separate the sample geographically.

The authors believe that this study can help researchers and investors more deeply understand the overall market and industry effects of significant terrorist and military events.

By offering a thorough examination of the differences between high-profile attacks in the context of stock returns both on the day of and the day immediately following those attacks, the authors hope that people will be able to better grasp the likelihood and magnitude of the initial damage done by these attacks as well as the subsequent recovery.

Most studies that examine the effects of terrorism on the stock market focus on one or two specific events or stock market locations. They also tend to concentrate on very specific characteristics of the attack(s) that they examine, such as the size of the market or the aggregate effect to that market. The authors study 28 high-profile events over a 50-year period and examine them by industry, country targeted, location, terrorism versus militarism and predicted overall impact. This study presents many new results using these classifications.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The purpose of the article is to contribute to the body of research on digital transformation among members of the supply chain operating in an emerging economy. This paper researches the digital transformation trends happening across Vietnamese supply chains, by investigating the current adoption rates, predicted impact levels and financial investments being made in key Industry 4.0 technologies.

By using a semi-structured online survey, the experiences of 281 supply chain professionals in Vietnam were captured. Subsequently, statistical techniques examining variances in means, regression analysis and Monte Carlo simulation were applied.

The findings of this study offer a comprehensive understanding of Industry 4.0 technology in Vietnam, highlighting the prevalent technologies being prioritized. Big data analytics and the Internet of things are expected to have the most substantial impact on businesses over the next 5–10 years and have received the most financial investment. Conversely, Blockchain is perceived as having less potential for future investment. The study further identifies several technological synergies, such as combining advanced robotics, artificial intelligence and the Internet of things to build effective and flexible factories, that can lead to more comprehensive solutions. It also extends diffusion of innovation theory, encompassing investment and impact considerations.

This study offers valuable insights into the impact and financial investment in Industry 4.0 technologies by Vietnamese supply chain firms. It provides a theoretical contribution via an extension of the diffusion of innovation theory and contributes toward a better understanding of the current Industry 4.0 landscape in developing economies. The findings have significant implications for future managerial decision-making, on the impact, viability and resourcing needs when undertaking digital transformation.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

The purpose of this paper is to examine the nature of strategic corporate communication (SCC) activities and its impact in Indian service sector organizations.

A descriptive research design was used with data obtained from 227 executives from service sector organizations. A research instrument was constructed and measures of SCC and its impact were derived through factor analysis.

Multiple regression analysis led to formulation of new relationships among the variables (messages, medium and stakeholders) involved in SCC and its impact. For example, in crisis situations, messages related to identity and image were associated with greater communication impact than were other types of messages. Similarly communicating with primary stakeholders like employees and customers was more important than with other stakeholders. Among the different types of medium used in SCC, virtual medium and disclosures led to greater communication impact.

Communication impact due to SCC was a multi-dimensional construct comprising of three kinds of impacts: communication synergy; value representation; and organizational reliability. The relationships of messages, mediums and stakeholders with different types of SCC Impact can help practitioners design and implement effective strategies of corporate communication.

The purpose of this paper is to explore whether people’s intuitions regarding the social consequences of word of mouth (WOM) match the actual consequences. The authors investigate the expectations people have about how sharing WOM (positive or negative) will change others’ perceptions of them and then compare these expectations to the actual impact of WOM.

Six studies were conducted. Study 1 predicted how sharing their experiences with various products or services would change others’ opinion of them. Studies 2a/2b contrasted participants’ intuitions about the potential social consequences of sharing WOM with the consequences. Studies 3a/3b and 4a/4b tested for the hypothesized mediating mechanism. Studies 5a/5b focused on negative WOM and used participants’ own reviews to compare intuitions with impact. Study 6 explored whether considering one’s own consumption experience mitigates the negative social impact of WOM.

Consumers expect positive WOM to improve perceptions as it conveys only positive cues about the communicator (i.e. helping intentions and a positive personality). Negative WOM is expected to have neutral impact, as it conveys mixed cues (i.e. helping intentions but a negative personality). In contrast, the authors show that sharing negative WOM tends to be quite detrimental, whereas sharing positive WOM has little impact. People are largely unaware of these effects.

The research contributes to the literature on WOM and social transmission by comparing people’s intuitions about the social consequences of WOM with its actual consequences. The authors acknowledge that they used mostly WOM messages that were pre-written (vs spontaneously generated by participants). This may have constrained the generalizability of the results. Several potential moderators remain to be investigated, such as the role of message extremity, the interpersonal closeness between communicator and receiver, whether the WOM was solicited vs spontaneous, online vs offline, etc.

Greater effort is needed to raise consumers’ awareness about the gap between their expectations and the actual social consequences of WOM. Furthermore, marketers responsible for designing product review opportunities should be encouraged to provide consumers with more flexible options, such as the ability to easily remove an online review. Finally, consumers transmitting negative WOM in particular should be aware that their negative tone may compromise the persuasiveness of their message by making the receiver more vigilant and thus less receptive.

The authors are the first to directly contrast people’s intuitions about the social consequences of WOM with its consequences. Unlike the previous literature, the authors investigate people’s intuitions directly, and investigate the consequences of positive and negative WOM by comparing them to a neutral no-WOM condition. They also shed light on the specific personality traits people infer from WOM.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran.

A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels. Various climate scenarios in Long Ashton Research Station Weather Generator were applied to predict weather data.

HadCM3 climatic model and A2 emission scenario were selected as the best methods for weather data generation. Based on the results of these models, annual precipitation will decrease by 3 per cent during 2015-2030. For three emission scenarios, i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall, precipitation in 2030 is estimated to be 265, 257 and 247 mm, respectively. For the studied aquifer, predicted recharge will decrease compared to recharge calculated based on the average of long-term rainfall.

The decline of groundwater level in the study area was 11.45 m during the past 24 years or 0.48 m/year. Annual groundwater depletion should increase to 0.75 m in the coming 16 years via climate change. Climate change adaptation policies in the basin should include changing the crop type, as well as water productivity and irrigation efficiency enhancement at the farm and regional scales.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

An economic impact study conducted in 2010 predicted that hosting the 34th America's Cup in 2013 would result in $1.37 billion in total economic benefits to the San Francisco Bay Area. The goal of this paper is to examine the ex post effects of this competition on real taxable sales in the Bay Area.

A panel data set of quarterly observations on taxable sales transactions for all counties in the state of California is employed. These data are explored using two estimation methodologies: difference-in-differences and synthetic control.

Results from a difference-in-differences analysis and a synthetic control analysis produce similar findings. Namely, the 34th America's Cup competition appears to have had a minimal, short-lived impact on San Francisco and no measurable impact on two nearby counties.

The empirical results in this paper underscore the findings of previous research showing that ex ante economic impact studies tend to overstate the net economic benefits of hosting mega-events.

The results of this paper may serve as a warning to policy makers considering using tax dollars to host a mega-event that such events often do not generate the economic gains reported in typical economic impact studies.

This is the first paper to econometrically explore the impact of hosting the America's Cup on taxable sales transactions in a region. This paper also employs the relatively new empirical methodology called synthetic control.