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The purpose of this paper is to propose a framework integrating the types and levels of the determinants of brand country of origin (CO) recognition and to provide evidence on internet users’ brand CO recognition rates, using a sample of multi‐regional and global brands from a variety of product categories and countries.

The authors integrate “level‐1” consumer and brand characteristics and “level‐2” product category and country effects in a single framework. Data obtained through an original on‐line survey hosted by Yahoo provide the basis for the empirical analysis. Seven hypotheses are tested using a two‐level cross‐classified random‐effect model (HCM2).

It is found that: education is positively related with brand CO recognition; experience with brands is positively related with brand CO recognition; integration between the consumer and the country of a foreign brand is positively related with brand CO recognition; internet users’ classification performance is significantly better for domestic than for foreign brands; brand‐name congruence with true brand origin is positively related with brand CO recognition; brand equity explains brand CO recognition; and product categories with higher consumer involvement enhance brand CO recognition. Brand CO recognition performance by internet users is in line with classification performance rates reported in other studies dealing with well‐known and global brands.

Managers would benefit from considering product category and country aspects of their most valuable brands. Policy makers should encourage firms to promote a clear association between brands and countries (when these countries have a positive image) and discriminate between high and low involvement product categories.

The paper contributes to the brand CO awareness literature by integrating consumer and brand characteristics in a theoretical model, and identifying level‐2 product category features and CO effects previously disregarded in brand CO recognition frameworks. In addition, the study positively contrasts with previous research by providing empirical evidence on brand CO recognition from the largest set of global brands (109), countries of origin (19) and product categories (15) ever investigated.

Responding in a timely manner to product recalls emanating from the supply chain presents tremendous challenges for most firms. The source might be a supplier from the same industry located next door, or one from a completely different sector of the economy situated thousands of miles away. Yet the speed of the firm’s response is crucial to mitigating the consequences of the recall both for the firm, and consumer health and well-being. The purpose of this paper is to investigate the effects of geographic distance, industry relatedness and clustering on firm response time to a supplier-initiated product recall.

The authors test the theoretical framework via an examination of food recall announcements registered with the US Food and Drug Administration over a ten-year period. The authors develop a data set comprising 407 pairs of supplier and affected downstream manufacturing firms, and utilize cross-classified hierarchical linear modeling to understand the drivers of organizational responsiveness.

The results suggest that firm response time is lengthened by geographic distance but reduced when the supplier and affected firm operate in related industry sectors. The authors further find that as more firms in a given industry are affected by the same recall, response time deteriorates.

Product recalls in the agri-food industry are significant events initiated to protect consumer health and ensure the safety of the farm-to-fork food chain. The findings highlight how both geographic- and industry-related factors determine the speed of firm responsiveness to these events.

The satisfaction and loyalty research argues that customer satisfaction is an antecedent to share of wallet (SOW). The double jeopardy view, however, argues that satisfaction and SOW levels are driven exclusively by penetration levels. Customer satisfaction and penetration, however, are not always positively related. The purpose of this paper is to explore the relevance and validity of these two divergent perspectives to creating growth in customer share of spending.

The authors examine a series of models evaluating the impact of both the relative penetration of a brand, and the satisfaction ratings of its customers on SOW using data covering 11 industry sectors, 188 brands, and 4,263 customers.

The authors find that part of the problem in reconciling these two views has been in how satisfaction is measured and analyzed. When using absolute satisfaction ratings of the firm/brand, the explanatory power of satisfaction on SOW is very weak at both the individual and firm level. When using satisfaction metrics relative to other competing brands, however, satisfaction is a strong predictor of customers’ share of category spending.

As predicted by double jeopardy, penetration is a strong predictor of firm-level SOW, but has almost no explanatory power at the individual level.

Managers need to focus on both improving penetration/reach and becoming the preferred brand in a customer’s usage set.

The research examines if (and if yes, how) satisfaction and penetration contribute to customers’ SOW allocations both at the individual and brand level.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose of this paper is to highlight challenges for service firms communicating Environmental, Social and Governance (ESG) efforts to customers. Specifically, it focuses on the relationship between ESG metrics and reporting and customer perceptions of social innovativeness.

The empirical material comprises three years of data (2018–2020) covering more than 100 firms from three sources: (1) Social Innovation Index (Sii), which is collected as part of the American Innovation Index (Aii), (2) Bloomberg Sustainability Accounting Standards Board (SASB) ESG and (3) Datamaran.

ESG metrics and reporting do not suffice to explain customer perceptions of social innovativeness. Rather, a firm's industry plays the prominent role in affecting these perceptions where service firms are at a disadvantage as customers perceive services as less socially innovative compared to goods.

While ESG metrics and reporting provide important information for investors and regulators, they are not reflected in customers' perceptions of firms' social innovativeness, and services are at a disadvantage relative to goods. Therefore, services researchers and managers must advance their knowledge regarding how to better link ESG metrics and report to customers' perceptions.

The paper offers a first large-scale, cross-industry investigation of how ESG metrics and reporting impact customer perceptions of social innovativeness, leading to a research agenda on communication of ESG.

The purpose of this paper is to analyze the influence of firm-, industry- and country-level determinants on real annual sales growth in the context of a cross-classified multilevel perspective.

The authors studied 11,381 firms from 17 industries in six Latin American countries based on the data collected up to 2015. Since the data are nested in two levels (level 1: firms; level 2: cross-classification of industries and countries), the authors use a cross-classified multilevel model. The significant variability in all levels of analysis confirms the option for the multilevel model.

Differences in industries account for the largest proportion of variance (77.2 percent). This finding indicates that industry-level characteristics should be explored in the sales growth literature (it seems to the authors that they were neglected). This finding also calls attention to the roles of policy-makers in facilitating firm growth. The final model indicates that the considered variables explain approximately 55 percent of the differences in real annual sales growth in the same industry and country after having accounted for the impacts of the differences in firms. After accounting for the impacts of the differences in firms’ and countries’ characteristics, 43 percent of the variation in average real annual sales growth is due to differences in industries. The obtained results indicate that while firms from countries with higher GDP growth and more effective corporate boards present higher real annual sales growth, firms that operate in commodity producer industries have worse performance in this indicator. With respect to firm’s characteristics, larger firms (contradicting Gibrat’s law) and exporters grew less. Some results could be explained by the decrease in commodities’ prices and global purchases between 2012 and 2015.

The paper fills some gaps in the firm growth literature by testing Gibrat’s law in non-developed countries (not yet done, to the best of the authors’ knowledge) and exploring variables other than size in the explanation of firm growth (rarely used, to the best of the authors’ knowledge). Moreover, the adopted model correctly estimated the origin of the variability in firm growth in its natural cross-classified distinct levels.

The purpose of this paper is to show how increasing teacher effectiveness is central to school efforts to improve student outcomes. This study aims to examine successive teachers' effects on student achievement. The premise advanced is that teacher effectiveness is an individual resource that varies across classrooms within schools, as well as a collective resource that varies across schools.

The methods used represent an attempt to expand the scope of previous studies about ways in which schools affect student learning by examining a multilevel constellation of teacher‐related effects (e.g. classroom effectiveness, collective teaching quality, school academic organization) that can be changed to increase educational effectiveness. The sample consisted of 9,196 students, cross‐classified in 511 and 527 classrooms, and nested in 156 elementary schools.

First, the effectiveness of successive teachers was related to student achievement in reading and math. Second, collective teacher effectiveness, as an organizational property of schools, was positively associated with achievement levels. Third, the stability of the school's teaching staff and the quality of its academic organization and teaching processes were positively related to achievement levels.

Findings are consistent with studies that have found that differences in teacher effectiveness matter in explaining student achievement. They also suggest that teacher effects tend to accumulate within and between schools to provide noticeable academic advantage or disadvantage. The results imply promising avenues through which a leadership focus on hiring and retaining high‐quality teachers and facilitating improved academic processes can yield increased school effectiveness.

The purpose of this paper is to test a multilevel, cross-classified model that seeks to illuminate the dynamic nature of relationships among leadership, teaching quality, and student learning in school improvement. The study's primary goal is to shed light on the paths through which leadership influences student learning. At the school level, the model examines the mediating effect of the school's instructional environment on leadership and student learning. At the classroom level, it examines how instructionally focussed leadership can moderate teacher effects on student learning. Then these multiple paths are examined in a single model that seeks to test and highlight the means by which leadership contributes to school improvement.

The current study employed a multilevel longitudinal data set drawn from 60 primary schools in one state in the USA. Using a cross-classification approach to quantitative modeling, the research analyzes the complex cross-level interactions that characterize school-level and classroom level practices that contribute to school improvement and student learning.

The results illustrate the utility of specifying multilevel relationships when examining the “paths” that link school leadership to student learning. First, leadership effects on student learning were fully mediated by the quality of the school's instructional environment. Second, the findings indicated that the classroom-related paths examined in this study directly influenced the measures of student math achievement. Third, the research found that instructionally focussed school leadership moderated the effect of individual teachers on student learning. Fourth, the results suggest that school leaders can enhance student outcomes by creating conditions that lead to greater consistency in levels of effectiveness across teachers.

The study makes substantive contributions to the global knowledge base on school improvement by testing and elaborating on the “paths” that link school leadership and student learning. More specifically, the findings offer insights into strategic targets that instructional leaders can employ to enhance teacher effectiveness and school improvement. Thus, these results both support and extend findings from prior cross-sectional and longitudinal studies of leadership and school improvement.

This is the first study that has tested a conceptualization of leadership for learning in a single “cross-classified longitudinal model” capable of capturing interactions among leadership, classroom teaching processes and growth in student learning. The research illustrates one “state-of-the-art” methodological approach for analyzing longitudinal data collected at both the school and classroom levels when studying school improvement.

This study investigates the relationship among preference for full-time employment, primacy of part-time employment, and work-related outcomes in a nationally representative sample of part-time college instructors. Results based on multilevel cross-classified random effects models indicate that part-time faculty who prefer full-time positions report working on average more hours per week and express greater work-related dissatisfaction than those who choose reduced work hours. Individuals whose part-time jobs are their primary jobs have less job satisfaction but work longer hours than those who treat part-time work as secondary. Finally, those who prefer full-time employment report more negative job satisfaction when the primacy of their part-time jobs is high.

This study examines how the characteristics of innovation knowledge exchanged among affiliate firms affect the ownership strategies adopted for their foreign subsidiaries.

This study employs a cross-classified multilevel model to examine a sample of 185 Korean manufacturing affiliates derived from 49 Chaebols engaged in international diversification, along with their 1,110 foreign manufacturing subsidiaries.

While exploratory innovation knowledge exchange lowers the affiliate's level of ownership in its foreign subsidiary, exploitative innovation knowledge exchange rather increases the affiliate's level of ownership in its foreign subsidiary.

This study advances the literature on intrafirm knowledge exchange by highlighting it as a determinant of ownership strategies. The study further shows that the characteristics of knowledge exchanged at the affiliate level not only determine the ownership structure but also have the potential to shape the direction in which the subsidiary develops its competencies.

This study has practical implications for the managers of business group affiliates. The results suggest that managers should adapt their ownership strategies according to the type of knowledge exchanged at the affiliate level to achieve a balanced and synergistic effect on intraorganizational knowledge exchange.

Previous studies have extensively explored the performance implications related to knowledge exchange. However, there is a notable gap in understanding the mechanisms through which the value of knowledge transferred within an affiliate is realized. To address this gap, this study focuses on ownership strategy as a crucial factor and empirically examines how the characteristics of innovation knowledge exchanged among affiliate firms influence the ownership strategies adopted for their foreign subsidiaries. By investigating this relationship, this study provides valuable insights into the complex dynamics of knowledge exchange and its effect on ownership decisions within business group affiliates.