Large-scale Field Data Research Articles

The Genesis Effect: Digital Goods in the Metaverse

Abstract This research shows that although the used and unused versions of a digital good (e.g., virtual apparel) are identical in every pixel and functionality, consumers tend to prefer the unused version. This “genesis effect” occurs because consumers tend to perceive used (vs. unused) digital goods as virtually contaminated and because being permanently listed as the first (vs. subsequent) owner in the ownership record can confer a greater sense of status. Specifically, in study 1, analyses of large-scale field data on purchases of digital goods in the metaverse showed that consumers paid substantially more to acquire the unused (vs. used) version of the same good. Studies 2–4 causally demonstrated the genesis effect and its underlying mechanism across metaverse product categories—participants were less likely to purchase digital goods described as used (vs. unused). Virtual contamination and virtual status jointly mediated the effect. Furthermore, being the first—at the genesis of a digital product’s usage history—was particularly special, such that participants were less sensitive to increases in the number of prior owners after the first one. Finally, showing participants that a used good had been digitally reconstituted attenuated the genesis effect. These findings add to the literature on consumer behavior in the metaverse and offer managerial insights on digital goods marketing.

재택근무가 업무 생산성에 미치는 영향과 업무 특성의 조절 효과

Purpose - This study aims to empirically identify the quantitative effects of work from home (WFH) on employee productivity using field data. Design/methodology/approach - Based on large-scale field data from a South Korean company which introduced the WFH arrangement in 2020, we conducted fixed effect and moderating effect analyses using individual-level panel data over sixty-three weeks. Findings - The empirical analysis generated several findings. It was found that overall, WFH has a positive effect on productivity. However, the productivity impact of WFH was found to vary depending on task characteristics. Specifically, WFH led to over 20% increase in productivity for simple and repetitive tasks. On the other hand, no significant productivity impact was observed for professional and knowledge-based tasks. Research implications or Originality - As the first study based on field data from South Korea, this study offers convincing causal evidence of the moderating impact of task characteristics on the relationship between WFH and productivity. Further, the above findings provide managers with practical insights concerning their work arrangement decisions.

Land Parcel Identification System (LPIS) data allows identification of crop sequence patterns and diversity in organic and conventional farming systems.

Farmers grow crops in specific sequences to lower disease pressure and boost crop productivity, particularly in organic farming where artificial pesticides and chemical fertilisers are prohibited. Knowledge about crop sequences used in organic and conventional farming will aid the development of future farming systems through optimising crop diversity and pre-crop effects for improved resource efficiency. This study aims to investigate crop diversity and patterns in organic and conventional crop sequences in Sweden. Large-scale LPIS field data managed by the European Union (EU) Integrated Administration and Control System (IACS) were used to monitor crop sequences on arable land in Sweden over 10 consecutive years (2005–2014). Individual fields (land parcels) could be followed on 40% of Sweden’s total arable area (349,891 fields extracted) over the 10 years. The LPIS data was combined with information from a database on which fields were farmed organically. Crop distribution, diversity of crop sequences and pre-crops to the main cereal crops (winter wheat, spring barley) were analysed in organic and conventional farming systems in the five agricultural productivity zones of Sweden. The results showed that in the most productive zone in southernmost Sweden, small-grain cereals (particularly winter wheat) were the most common crops (62%), followed by oilseeds (11%), ley and forage crops (9%) and sugar beet (8%), when excluding permanent grassland. In the least productive zone (at higher altitudes and/or latitudes), ley and forage crops dominated (67%), followed by spring cereals (barley, oats) (23%). Crop diversity was higher in the two more productive zones (mean 4.6 crop types) than in two less productive zones (3.4) and organic farms showed 9% higher crop diversity than conventional farms in the most productive zones. Overall, in all zones, the pre-crop to winter wheat was generally a different crop type (3 out of 5 times) e.g., young ley (1–2 years) or grain legume, while the pre-crop to spring barley was most often (4 out of 5 times) another cereal. For both these crops, pre-crop type was more diverse in organic than conventional systems. These findings demonstrate that LPIS data can offer valuable insights into agronomic trends and on-farm practices regarding crop choice and that analysis of field-level LPIS data on crop sequences at large scale can reveal information about organic and conventional cropping in different productivity zones across countries. This information can be used to understand the practical limitations in the use of crop diversity to maximise pre-crop effects. This could in turn support advisory service and policy makers to facilitate more sustainable, productive and resource efficient crop production.

The Art of Slowness: Slow Motion Enhances Consumer Evaluations by Increasing Processing Fluency

Slow motion is a popular video editing tool used to enhance short-form videos (e.g., reels, stories, GIFs), which are commonly found in media entertainment and marketing communications. This research shows that slow motion increases the virality (e.g., likes, votes, views) of short-form videos and boosts brand liking, choice, and willingness to pay. The effect occurs because slow motion enhances the hedonic component of the viewing experience via processing fluency. By documenting how the success of slow motion is subject to moderators, this work shows marketers, entertainment producers, and everyday people how to use slow motion more effectively. Across a large-scale field data set and six experiments, the authors highlight that slow motion is especially effective when applied to short-form videos that are inherently pleasant and that involve complex movements that are difficult to perceive at regular speed. However, even simple movements benefit from slow motion when content creators zoom in on subtle movements to increase complexity. Moreover, slow motion is more effective when viewers engage in less elaborate processing. Finally, the authors show that the perceived disfluency of fast-motion editing is effective at boosting brand evaluations when viewers desire excitement.

Economic costs of the invasive Yellow-legged hornet on honey bees

Biological invasions have ecological impacts worldwide with potential massive economic costs. Among other ecosystem services such as nitrogen cycle, carbon sequestration and primary production, invasive alien species are particularly known to impact pollination. By predating honey bees (Apis mellifera), the invasive Yellow-legged hornet (Vespa velutina nigrithorax) increases the mortality risk of European bee colonies; however, little is known about its economic costs. We developed an analytic process combining large-scale field data, niche modelling techniques and agent-based models to spatially assess the ecological and economic impacts of the Yellow-legged hornet on honey bees and beekeeping in France. In particular, we estimated (i) the hornet-related risk of bee colony mortality, (ii) the economic cost of colony loss for beekeepers and (iii) the economic impact of livestock replacement compared to honey revenues at regional and national scales. We estimated an overall density of 1.08 hornet nest/km2 in France, based on the field record of 1260 nests over a searched area of 28,348 km2. However, this predator density was heterogeneously spread out across the country as well as the distribution of managed honey bee colonies. Overall, this hornet-related risk of bee colony mortality could reach up to 29.2 % of the beekeepers' livestock at national scale each year in high predation scenario. This national cost could reach as much as € 30.8 million per year due to colony loss, which represents for beekeepers an economic impact of livestock replacement of 26.6 % of honey revenues. Our results suggest non-negligible ecological and economic impacts of the invasive Yellow-legged hornet on honey bees and beekeeping activities. Moreover, this study meets the urgent need for more numerous and accurate economic estimations, necessary to calculate the impact of biological invasions on biodiversity and human goods, with a view to enhance policies of biodiversity conservation.

Spherical Planting Inversion of GRAIL Data

In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds’ density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the “shape-of-anomaly” data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin.

Energy dissipation model for irregular breaking waves owing to air bubbles

Based on the air bubbles effect, an irregular energy dissipation model for plunging and spilling breaking waves has been developed theoretically. To develop the model, the probability of fraction of breaking wave phenomenon has been applied. The developed model has been numerically solved for irregular beaches and analytically solved for plane sloping beaches. The energy balance and the momentum balance equations are used to calculate the root mean square (RMS) wave height and the wave set-up, respectively. To validate the new model, a large number and a variety of test circumstances, involving small-scale, large-scale, and experimental field data, have been employed. Finally, the intended models execute a smaller amount of normalized RMS error (NRMSE) in calculating RMS wave height as well as wave set-up compared with the different categories of experimental data.

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R 2 , root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R 2 ranging between 367 and 470 kg ha −1 , 276–345 kg ha −1 , and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha −1 for nitrogen application rate to 372 kg ha −1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha −1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data. • Data-driven agronomic research requires new analytical and methodological approaches. • Machine learning methods were used to disentangle complex relationships in farmer field data. • Model-agnostic tools were used to derive agronomic interpretations. • Residue management and N application rate were important management variables for wheat yield. • Residue management interacted with other practices to explain wheat yield variability.

An efficient 3D wave-equation pre-stack time migration for high-density and wide-azimuth data

Abstract The sharp increase in data size of the high-density and wide-azimuth seismic acquisition makes seismic processing routines very challenging. To reduce the turn-around time of pre-stack time migration (PSTM) for large-scale field data (terra to petabyte size), we present an efficient wave-equation PSTM that is an approximate version of the double-square-root (DSR) migration for moderate lateral velocity variation. By converting the DSR dispersion relation into the vertical-time domain and ignoring an offset-dip term, a novel common-offset ray parameter wave-equation PSTM is developed. Meanwhile, the common-offset ray parameter data (i.e. offset plane waves) can be formed by applying the linear Radon transform to the common middle point CMP) gathers. To enhance the signal-to-noise ratio of the conventional Radon transform, we integrate the Radon transform result over azimuth, yielding azimuth-independent offset plane waves that are well suited to irregular sampling and uneven offset distribution. Furthermore, the split-step Fourier method is introduced to extrapolate the wavefield efficiently. Since the pre-stack migration of common-offset ray parameter data is migrated independently, the common image gathers (CIGs) can be generated naturally by sorting the migrated sections by ray parameters. A 3D high-density field data test demonstrates the efficiency and effectiveness of the proposed method, making it very promising for industrial-sized data processing.

An enhanced hybrid deep neural network reduced-order model for transonic buffet flow prediction

In the transonic flight environment, transonic buffet has significant influence on the aerodynamic characteristics of aircraft. In the face of the strong nonlinear characteristics of transonic buffet, the reduced-order model (ROM) technology based on deep learning (DL) is introduced to study the flow field. In this article, an enhanced hybrid deep neural network (eHDNN) consisting of Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (ConvLSTM) neural network is constructed as the ROM of unsteady transonic buffet flow field. By mining the spatial-temporal characteristic of buffet flow directly from large-scale flow field data, the eHDNN can predict complex flow characteristics such as shock wave motion in the unsteady buffet flow. Structural similarity information and nonlinear improvement strategies are introduced to improve the nonlinear expression ability of eHDNN. The predicted result is consistent with the numerical simulation and has high degree of prediction accuracy. In addition, the eHDNN has good generalization ability, which can predict the spatial-temporal evolution of buffet flow field at unknown angles of attack. The prediction method has good application prospect of the transonic buffet flow control and prediction, which also provides experience for DL modeling in other complex and strongly nonlinear flows.

TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network

The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as the Actions on Objectives campaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.

How do ethical consumers utilize sharing economy platforms as part of their sustainable resale behavior? The role of consumers’ green consumption values

• This study helps us understand how the consumption values that lead to using a second-hand peer-to-peer platform affect green consumption practices and sustainable resale behavior. • Economic and practical values negatively affect green consumption values and subsequently weaken the consumers’ preparedness to engage in sustainable resale behavior. • Recreational, generative, societal benefit, and protestor values positively influence green consumption values and increase the consumers’ willingness to engage in pro-environmental behavior. • Gender moderates the relationship between green consumption values and the consumption values of using a peer-to-peer platform. Past research has extensively studied the antecedents and consequences of consumers’ green consumption values, as well as the psychological mechanisms that underlie an ethical consumer. Yet a frustrating paradox remains, indicated by the consumers’ intention–behavior gap for their sustainable behavior. To address this gap, the present study focuses on the consumption values that lead to using a sharing economy platform. Our study draws on the theory of consumption values and altruistic–egoistic values, as well as spillover effect psychology, to examine associations between context-specific values, green consumption values, and sustainable resale behavior. By collaborating with a Nordic second-hand peer-to-peer platform brand, our findings—obtained from large-scale field data ( n = 3256)—challenge the conventional wisdom by demonstrating that economic and practical values for using the second-hand peer-to-peer platform negatively affect green consumption values and subsequently weaken the consumers’ preparedness to engage in sustainable resale behavior. In contrast, recreational, generative, societal benefit, and protestor values positively influence green consumption values and increase the consumers’ willingness to engage in pro-environmental behavior. Further, such relationships are moderated by gender: stronger effects were identified among female consumers. These findings have important implications for theory and practice.

Data-Driven Time-Frequency Method and Its Application in Detection of Free Gas Beneath a Gas Hydrate Deposit

The time-frequency (TF) analysis method plays a significant role in the detection of natural gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in the TF methods due to the sparsity of the TF representation. This study proposes a data-driven TF method based on the CS theory and a non-convex regularization. In the implementation, a continuous wavelet transform (CWT) with a generalized beta wavelet (GBW) is formulated as an inverse problem based on the CS theory. The selection of appropriate parameters enables the GBW to match the seismic wavelets better than the widely used Morlet wavelet. The GBW can constitute a tight frame to reduce calculation time, particularly for large-scale field data processing. Additionally, the proposed TF method introduces the generalized minimax concave (GMC) penalty function as a non-convex regularization term. Compared with the classical sparse approximation method with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1} $ </tex-math></inline-formula> regularization, the GMC regularization term can enhance the sparsity in sparse inverse problems and ensure the convexity of sparse inversions. This article also presents an exponentially decreasing threshold scheme to adaptively select the regularization parameters. Three synthetic examples are investigated to demonstrate the performance of the proposed sparse TF representation with GMC regularization. Finally, the proposed TF method’s performance in detecting free gas of gas hydrates is validated using field seismic data obtained from the Blake Ridge.

Earth current and charge accumulation in the electrical interface during geomagnetic storm using largescale geo-electric field data

Earth current and charge accumulation in the electrical interface during geomagnetic storm using largescale geo-electric field data

Earth current and charge accumulation in the electrical interface during geomagnetic storm using largescale geo-electric field data

Earth current and charge accumulation in the electrical interface during geomagnetic storm using largescale geo-electric field data

Do Consumers Order More Calories in a Meal with a Diet or Regular Soft Drink? An Empirical Investigation Using Large-Scale Field Data

Diet carbonated soft drinks (CSDs) were introduced to help consumers lower caloric intake. However, critics suggest that these drinks can provide an excuse to consume more calories, a so-called “Big Mac and Diet Coke” mentality that is consistent with behavioral theories such as moral licensing (e.g., combining a healthy eating choice with an indulgent, less healthy one). Using individual-level food and drink consumer panel data from a major fast-food restaurant chain, the authors empirically examine meals with a regular CSD versus a diet CSD. Results after controlling for drink size and demographics show that consumers generally do not order higher total calories from a meal with a diet CSD; rather, the authors find significant reductions in calorie count, suggesting that within a single meal, diet CSDs can help consumers unwilling to stop drinking CSDs to reduce calories. So, despite popular beliefs to the contrary, policy makers can consider diet CSD availability as a “calorie-reduction” strategy to lower calorie consumption within a meal.

Nothing to Worry About: Why Liberals Underestimate Dominant Leaders and Act Complacently

When dominant-authoritarian leaders are successful, political liberals overwhelmingly express shock and disappointment. Beyond party affiliation, we offer a psychologically grounded explanation contending the prevalence of a systematic bias among liberals. We argue that liberals, compared to conservatives, underestimate the success of dominant-authoritarian leaders, and that this systematic underestimation makes liberal complacent, overconfident, and less inclined to exercise their vote. We test our hypotheses across seven studies (two pre-registered, one in SI), including large-scale field data, six experimental studies with varied contexts, and a combined sample of more than 215,000 observations from 93 countries and spanning the past three decades. In doing so, our work explains the continued underestimation of dominant leaders and consequent surprise from political liberals during critical elections, and why underestimating the success of dominant leaders may lead liberals to act complacently and paradoxically increase such leaders’ chances of success.

What is beautiful is not always good: influence of machine learning-derived photo attractiveness on intention to initiate social interactions in mobile dating applications

The popularity of mobile dating applications has reconstructed how people initiate new romantic relationships. Photo attractiveness, the most prominent information provided in the online dating context before social interactions, has attracted considerable attention but reached inconsistent conclusions. By considering the location, gender, and attractiveness difference between each dyad of participants (hunter and target) together, this study attempts to re-examine the contingent impact of physical attractiveness in initiating social interactions. Multi-methods (machine-learning and panel logistic regression) were used to empirically analyse the large-scale field data. The results show that: (1) A target’s photo attractiveness can promote a hunter’s willingness to leave a message; (2) The impact of photo attractiveness will be attenuated when two users have a larger location difference; (3) Male users are more likely to be impacted by photo attractiveness in their social interactions than female users; (4) A hunter with low attractiveness is inclined to initiate social interactions with a more attractive target; whereas a hunter with high attractiveness does not. This study deepens the theoretical implications in relevant fields, provides new methodology insight, and offers personalised marketing strategies for mobile dating applications.

Species occurrence relates to pesticide gradient in streams

Freshwater communities are threatened worldwide, with pesticides being one of the main stressors for vulnerable invertebrates. Whereas the effects of pesticides on communities can be quantified by trait-based bioindicators such as SPEARpesticides, single species’ responses remain largely unknown.We used the bioindicator SPEARpesticides to predict the toxic pressure from pesticides in 6942 macroinvertebrate samples from 4147 sites during the period 2004 to 2013, obtained by environmental authorities in Germany, and classified all samples according to their magnitude of pesticide pressure. Along this gradient of pesticide pressure, we quantified the occurrence of 139 macroinvertebrate species.We identified 71 species characterized by decreasing occurrence with increasing pesticide pressure. These ‘decreasing species’, mainly insects, occurred at a frequency of 19.7% at sites with reference conditions and decreased to 1.7% at sites with the highest pesticide pressure. We further determined 55 ‘nonspecific species’ with no strong response as well as 13 ‘increasing species’, mainly Gastropoda, Oligochaeta and Diptera, which showed an increase of frequency from 1.8% at sites with reference conditions to 11.4% at sites with the highest pesticide pressure. Based on the change in frequency we determined the pesticide vulnerability of single species, expressed as Pesticide Associated Response (PARe). Furthermore, a trait analysis revealed that species’ occurrence may additionally depend on oxygen demand and, to a lesser extent on substrate preference, whereas no significant effect of feeding and respiration type could be found.Our results provide the first extensive pesticide vulnerability ranking for single macroinvertebrate species based on empirical large-scale field data.

Customer attrition analysis in the securities industry: a large-scale field study in Korea

PurposeThe purpose of this paper is to identify the attributes that predict customer attrition behavior in the brokerage and investment banking sectors.Design/methodology/approachThe authors analyze the complete stock trading records and customer profiles of 458,098 retail customers from a Korean brokerage house. The authors develop customer attrition prediction models and further explore the practicality of these models using statistical classification techniques.FindingsThe results from three different binary selection models indicate that customer transaction patterns effectively explain the attrition of active retail customers in subsequent periods. The study results demonstrate that monetary value variables are the most critical for predicting customer attrition in the securities industry.Research limitations/implicationsThis study contributes to the customer attrition literature by documenting the first large-scale field-based evidence that confirms the practicality of the canonical recency, frequency and monetary (RFM) framework in the investment banking and brokerage industry. The findings advance previous survey-based studies in the financial services industry by identifying the attributes that predict customer attrition behaviors in the securities industry.Practical implicationsThe outcomes can be easily operationalized for attrition prediction by practitioners in financial service firms. Moreover, the ex post density of inactive customers in the top 10 percent most-likely-to-churn group is estimated to be five to six times the ex ante unconditional attrition ratio, which ascertains that the attributes recognized in this study work well for the purpose of target marketing.Originality/valueWhile the securities industry is regarded as one of the most information-intensive industries, detailed empirical investigation into customer attrition in the field has lagged behind partly due to the lack of suitable securities transaction data and demographic information at the customer level. The current research fills this gap in the literature by taking advantage of a large-scale field data set and offers a starting point for more elaborate studies on the drivers of customer attrition in the financial services sector.