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The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The internet has become mainstream in everyday communications and transactions. This research aims to provide a segmentation analysis for the online market based on the various uses of the internet.

A review of the online consumer segmentation literature is first conducted. Survey method and cluster analysis techniques are used in the empirical study. A sample of 407 participants that belonged to a large consumer panel adequately responded to an online survey and provided their pattern of internet use, internet experience, and psychological characteristics.

The analysis shows that the online consumers form three global segments: the basic communicators (consumers that use the internet mainly to communicate via e‐mail), the lurking shoppers (consumers that employ the internet to navigate and to heavily shop), and the social thrivers (consumers that exploit more the internet interactive features to socially interact by means of chatting, blogging, video streaming, and downloading). Subsequent χ² and ANOVA tests illustrate that consumers from these segments exhibit significantly divergent demographic and experience profiles.

The results indicate that online consumers differ according to their pattern of internet use. The results have external and ecological validity; however, they lack the control provided in a laboratory experiment. Future research should examine if the findings can be replicated using behavioral measures.

Practitioners that plan to follow a resource‐based approach should consider the distinctive characteristics of the online market segments for an optimal allocation of marketing expenditure. Marketing and advertising strategies can be developed according to the customer's online segment. Further, online marketers can use the demographic and experience profiles to predict their customer's segment.

This paper is the first to perform a segmentation analysis to the online consumer market according to internet use pattern. The results show that usage can reliably be used as a segmentation base. Managerial and theoretical implications are furnished.

To classify internet users into holiday shopper and non‐shopper segments, and to profile the demographic, psychographic, and computer use characteristics of each segment.

Self‐report data come from a national US sample of online internet users. Segments are customer revealed using traditional cluster analysis. Lifestyle measures are reduced to higher order measures using factor analysis. Profiles are analyzed via descriptive statistics, graphs, and radar charts.

Six important segments are identified in the data. Three of the segments characterize customers who resist online shopping, even though they engage in other online activities. Security fears and technological incompetence typically inhibit these users from engaging in electronic exchange. Some internet users simply choose not to shop online. Three of the segments describe active e‐shoppers who are driven by a unique desire to socialize, minimize inconvenience, and maximize value.

Data come from self‐report questionnaires administered and collected electronically through the internet. Focus is placed on holiday gift buying. Since, holiday shopping is very important to e‐retailers, results are managerially interesting, but might not be indicative of other shopping periods.

To be successful, e‐retailers must understand those things that motivate and inhibit customer online shopping. Marketing activities targeted at reticent e‐shoppers should focus on benefits, guarantee safeguards and facilitate technical literacy. Service, value, and online ambiance should be carefully tailored to meet the desires and expectations of each customer type.

The study is a replication and extension of earlier online studies which are summarized in the reviewed literature.