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This study aims to assess whether similar user populations in the Internet produce similar geographical traffic destination patterns on a per‐country basis.

The authors collected a country‐wide NetFlow trace, which encompasses the whole Spanish academic network. Such a trace comprises several similar campus networks in terms of population size and structure. To compare their behaviors, the authors propose a mixture model, which is primarily based on the Zipf‐Mandelbrot power law to capture the heavy‐tailed nature of the per‐country traffic distribution. Then, factor analysis is performed to understand the relation between the response variable, number of bytes or packets per day, with dependent variables such as the source IP network, traffic direction, and country.

Surprisingly, the results show that the geographical distribution is strongly dependent on the source IP network. Furthermore, even though there are thousands of users in a typical campus network, it turns out that the aggregation level which is required to observe a stable geographical pattern is even larger.

Based on these findings, conclusions drawn for one network cannot be directly extrapolated to different ones. Therefore, ISPs' traffic measurement campaigns should include an extensive set of networks to cope with the space diversity, and also encompass a significant period of time due to the large transient time.

Current state of the art includes some analysis of geographical patterns, but not comparisons between networks with similar populations. Such comparison can be useful for the design of content distribution networks and the cost‐optimization of peering agreements.

The purpose of this paper is discussing on cross-city empirical economic growth, by estimating an unbalanced dynamic panel for the most vulnerable region of Brazil.

The authors propose including additional and specific sources of cross-city variation, enabling them to capture the essence and reality of this region. The sample selection is given by the solution of a trade-off on the number of cities and the available explanatory variables. Considering the final choice, the analysis is based on 6,452 observations extracted from a sample of 925 cities between 2009 and 2015. Reconciling the regional growth literature and this availability of observable data, the authors decide to explain cross-city real gross domestic product per capita in log, controlling for its lagged value besides 15 explanatory variables on human capital, financial system, business environment and social infrastructure.

This study uses growth drivers on human capital, financial system, business environment and social infrastructure. Considering 6,452 observations for the period from 2009 to 2015, this study finds a significant role played by the levels of education of formal workers, rural financing, real estate financing and FIRJAN indices (health and employment).

A more comprehensive and complete understanding of cross-city variation, whether in the Northeast, in the North of the country or in other regions, involves the expansion of growth drivers in the model. Certainly, the impact of the industrial sector (not captured by the FIRJAN employment/income index), or programs and initiatives geared to technology, must be significant and positive. Despite the low market share, the insertion of microcredit data for informal, small business owners and more underserved families, can bring insights not measured in this article.

On financial system and development: The results on the significant and positive coefficient of rural and real estate financing are fundamental in conducting public policies aimed at granting credit. On human capital: The expected and intuitive relevant role of education suggests that good policies that are implementable need to be looked for and replicated to other northeastern cities. The state of Ceará seems to be that benchmark to be followed by the other states.

Another public policy that needs to be strengthened so that the most vulnerable cities can grow is related to the partnership with the private sector in the expansion and maintenance of basic sanitation. In this context, the new Legal Framework for Basic Sanitation is an important step. Its main objective is to universalize and qualify the provision of services in the sector. Theoretically, it seems to be an important advance and this also unlocks the first big wave of investments.

The analysis aims to contribute to the recent studies on regional growth applied to Brazil. To the best of the authors’ knowledge, this is an innovative contribution, and the main differences between this paper and the others are the sample of cities, the period, the growth model and the estimation technique. For instance, Da Mata et al. (2005, 2007) explore population growth and its implications for economic dynamics and income generation among 123 urban agglomerations between 1970 and 2000. Alves (2021) studied slum growth in contemporary urbanization of households in 272 Brazilian cities from 1991 to 2010.

The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system.

Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app.

Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product.

Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study.

The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost.

This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

The growing displacement of theory and other forms of wide-ranging knowledge of social phenomena by empirical research methods in economics is widely noted by economists and historians of economic knowledge. Less attention has been devoted, however, to understand the materialization of such changes in the scientific practices. This article studies the recent transformations in the epistemological practices at CEDE, a research center in Colombia. I use a machine learning technique called Topic Modeling, interviews to CEDE researchers, and exegesis of papers to characterize a shift in the production of knowledge in microeconometrics at CEDE during the years 2000 and 2018. I explain this shift by characterizing two sets of epistemological practices that implies a recent tendency to disdain research that cannot make a “strong” causal inference.

Scientific production on the topic of information technology (IT) capabilities grows each year, leading researchers working in the field into difficulties to keep abreast of the new research flows, as well as to know the intellectual and conceptual structures that sustain the area. Thus, this study aims to present the result of the application of a quantitative approach on the international scientific production on the subject.

Several complementary bibliometric techniques were combined, such as keywords co-occurrence, bibliographic coupling and cocitation analysis. Considering that the bibliometric research support software currently available to the academic community has strengths and weaknesses, the present study uses three recent tools of analysis (VOSviewer, SciMAT and Bibliometrix), to explore the best that each can offer.

The authors analyzed 219 scientific papers available on the Web of Science. The authors identify the origins of the current literature on IT capabilities, as well as its foundations and main intellectual and conceptual structures, including a longitudinal view of the development of the theme. The paper also presents the main research fronts in the field, as well as several opportunities that can contribute to the advancement of research involving IT capabilities.

The fundamental themes for structuring the whole of the scientific production on IT capabilities, which had a higher level of development and connection with external research flows, are organizational performance, strategic management, resource and value-based view. Thus, future researchers will find underlying literature on IT capabilities in the context of business a rich and varied literature involving these major themes.

Pulque is a fundamental piece in the Mexican gastronomy and identifying consumers’ perception could help promote it and generate new opportunities of commercialization. The purpose of this paper is twofold: first, to explore the underlying factors that influence consumers’ perception toward pulque, and second, to identify groups of consumers according to their perception.

A questionnaire was designed and applied to 220 consumers within points of sales. Exploratory factor analysis (EFA) was undertaken and hierarchical cluster analysis (HCA) was used to identify different groups of consumers.

By means of EFA three factors were obtained: traditionality, bad reputation of pulquerías and functional beverage. In order to identify different consumption patterns among consumers, HCA was performed and three groups were identified: traditional, neo-consumers and neo-consumers in transition. The groups identified showed significant differences in their perception.

Pulque has recently gained interest in various consumer sectors, mainly among young consumers, who are consuming and promoting the beverage these days. In this sense, the results of this work could be used for its promotion and consumption in order to attract more consumers and make a contribution to the development of local economies.

This is the first work in Mexico to explore the perception of consumers toward pulque: the most important traditional fermented beverage in Central México. Results of this research suggest the appearance of a revaluation process among young consumers, more informed and with greater sensitivity toward traditional Mexican food, particularly in the case of pulque. Likewise, these results could be useful for producers and sellers to carry out commercial strategies and promote the drink.

As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms’ agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance.

This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling.

The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm’s agility. This paper also found out that industry dynamism moderates the firms’ agility and, ultimately, firms’ performance.

The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism.

The present research is to the best of the authors’ knowledge among the first studies considering a firm’s agility to explore the impact of BA on a firm’s performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility.

The purpose of this paper is to analyse the determinants of technical efficiency (TE) in dairy farms located in the South of Brazil, aiming for a better understanding of the topic for academics, dairy farmers and policymakers to improve the productivity and competitiveness of dairy farms.

This study was developed using a two-stage approach. Data envelopment analysis was used to estimate the TE level and regression models to understand the factors affecting TE in dairy farms. The sample size is 253 dairy farms in the South of Brazil.

The variation in the mean TE indexes reported in the literature can be explained by the attributes of the analysed studies, including the education of the farm operator, farm size (number of cows and milk), feed and labour costs, and use of services. Additionally, the results suggest that dairy farmers in the sample could increase milk output by 50.1 per cent (level of inefficiency) on average if they improve their TE.

This study makes three important contributions: first, it formulates hypotheses from the previous literature’s propositions on the estimation of TE in dairy farms; second, it tests the hypotheses in an empirical study to understand the main factors affecting the TE in dairy farms of the selected municipalities in the South of Brazil; and third, it compares previous findings on the determinants of TE in dairy farms serving different stakeholders, such as researchers, farmers and government representatives, to improve the productivity and competitiveness of dairy farms.