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The purpose of this paper is to extend the developing body of knowledge on supply chain performance by addressing the impact of resource commitment (RC), product route efficiency (PRE), and manufacturing flexibility (MF) on a firm’s financial performance (FP) has a direct impact on supply chain operations.

Survey questionnaires were developed in conjunction with literature guidance. Exploratory and confirmatory factor analysis was used in conjunction with structural equation modeling to give a robust analysis of the problem setting.

Discoveries herein indicate that committing resources in itself is insufficient to adequately increase FP over the long term. However, the mediating variables of MF and PRE were found to significantly improve a firm’s bottom line.

Prior research has been somewhat lacking and inconsistent with regards to the nature of causal and mediating relationships found between RC, PRE, MF, and a firm’s financial performance. Given the increasing global nature of competition, understanding the relationships between potential factors that could positively impact a firm’s FP has a large potential direct impact and benefit on supply chain operations.

This paper coins the construct of Service Orientation (SO) and empirically develops its measurement in the context of business‐to‐business (B2B) e‐commerce. SO is operationally defined as the business' overall propensity for delivering service excellence. The purpose of this paper is to demonstrate that multi‐item measurement scales have sufficient psychometric properties of validity and reliability to be useful for theory building and testing.

The authors followed Menor and Roth's two‐phased approach to develop new multi‐item measurement scales. First, the authors reviewed the literature, held structured interviews with managers and performed six independent rounds of item‐sorting analyses to obtain insights for the initial measurement model specification. Second, survey research procedures were employed to develop and refine a questionnaire to collect data on a sample of senior managers of 181 US businesses that implemented B2B e‐services. The psychometric properties of the SO dimensions were confirmed using structural equations modeling.

The authors empirically confirm the nomological network of SO as a third‐order latent variable comprised of five combinative service competency bundles: service climate; market focus; process management; human resource policy; and metrics and standards. Together these bundles provide a holistic and integrative representation of the general operating environment's orientation towards customers and a business' general propensity to deliver service excellence. Importantly, the measurement structure of service orientation was found to be invariant for both goods producing and service firms.

The proposed metrics are a useful benchmarking tool for practitioners from both manufacturing and service firms to use to monitor and improve their business's SO.

The paper is believed to be the first to operationally define and measure SO in the context of B2B e‐commerce.

An employee attitude survey can be described as a diagnostic tool which collects large amounts of information under controlled conditions. It acts as a vehicle for communication, allowing employees to express their satisfaction and ideas for improvement as well as offering management unique and practical information to assist the problem‐solving process.

This paper is concerned with estimation of the parameters of a high-frequency VAR model using mixed-frequency data, both for the stock and for the flow case. Extended Yule–Walker estimators and (Gaussian) maximum likelihood type estimators based on the EM algorithm are considered. Properties of these estimators are derived, partly analytically and by simulations. Finally, the loss of information due to mixed-frequency data when compared to the high-frequency situation as well as the gain of information when using mixed-frequency data relative to low-frequency data is discussed.

The purpose of this study is to examine the relevance of American Customer Satisfaction Index (ACSI) to management voluntary forecasts of earnings. The authors further investigate whether the market reacts to such forecasts in respect of satisfaction.

The authors’ econometric models are constructed from previous work in accounting to specify the effect of ACSI on the issuance and optimism of management forecasts. Our model also specifies the impact of management optimism with respect to ACSI on stock returns. The data consisting of US firms in the 2001-2010 is collated from several databases and analyzed using multiple regression procedures.

Results indicate that ACSI is positively associated with the likelihood of issuing management forecasts and boosts management optimism. It is also found that investors react negatively to management optimism that is inherent in forecasts and results from satisfaction.

The authors’ research findings not only complement prior work on the linkage between customer satisfaction and firm value by incorporating a managerial perspective but also respond to the recent call for further work on how relevant marketing metrics drive organizational decisions and firms’ financial performance. It should be noted that findings are limited to firms that release both a voluntary issuance of management forecasts and ACSI.

The study results shed light on the justification of marketing expenditures and provide a response to the call for marketing accountability. The study results also enable managers to make better decisions about whether and when to issue a forecast. The authors’ research further calls stakeholders’ attention to the presence of management forecast optimism with respect to satisfaction.

Despite the importance of managers as primary information generators and disseminators in the capital markets, there appears to be little discussion on the satisfaction’s relevance to market participants, particularly in relation to the role of managers. Therefore, this investigation is the first to empirically show the relevance of ACSI to management earnings forecasts that have been ignored in the marketing literature.

This study aims to increase the understanding about the concept of high-quality connections (HQCs) by examining how it develops between members of different generations.

It draws on film-based method to explain how the theory of HQC unfolds, particularly in a context of high intergenerational connection.

This study reflects the analysis of a movie that, to a large extent, fits in the HQCs conceptual framework. Rather, all key mechanisms of HQCs concept (i.e. behavioral, cognitive and emotional) were found in the plot of Die Hard 4.0. It provides compelling evidence that films may explore the possibilities of reality including the nuances of the relationships among human beings, despite the fact that it portrays a fictional creation. In doing so, it shows that HQCs are likely to be found – as very often happens in the real life – even in the worst situations. Findings also show that HQCs may be built through the time as individuals start to better know each other, i.e. their styles, behavior, religion preferences, held values, capabilities and interpersonal skills.

Otherwise, film method does not allow that the research results be generalized. At best, it offers elements to the viewers reflect about.

Very often individuals are designated to carry out certain tasks along with unknown colleagues with whom they need to interact in a positive manner to accomplish the goals. As a result, organizations should pay close attention to the quality of connections among their employees.

To the best of the author’s knowledge, this is the first endeavor toward analyzing the mechanisms of HQCs concept by means of such approach. In doing so, this analysis strongly corroborates HQCs concept functioning.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

This paper examines mechanisms through which social capital strengthens microfinance impact on fostering female entrepreneurial success. Specifically, the study focuses on how, and to what extent, resources embedded in social networks determine MF impact on entrepreneurial success.

Survey data were collected from 276 female micro-institutions entrepreneurs using multi-stage stratified random sampling across 80 MF institutions in three South-Western Nigerian states. Hypotheses were tested using ordinal regression analysis.

The study found that relational and network social capital had a positive and significant influence on female entrepreneurial success. Specifically, intra-group trust and productive network ties amongst female entrepreneurs in poor communities predicated the positive impact of MF on entrepreneurial success. Also, resources embedded in networks are more positively correlated to education level and marital status. Furthermore, MF could have more positive impact for borrowers with sustainable relationships with loan officers who organise MF provisions and understand the entrepreneurs’ context.

The research provides empirical evidence for the relationship dynamics between female entrepreneurs and MF institutions, by emphasising the importance of deploying different forms of social capital in sustaining MF impact on female entrepreneurial success.

The authors examine the influence of powerful political corporate appointments on the usage of firm bribery channels. Party Secretaries within Chinese state-owned enterprises (SOEs) may simultaneously hold top management positions, thereby endowing powerful firm-level decision rights on those appointees, hereafter referred to as powerful dual role Party Secretaries.

This study employs panel data analysis with industry and year fixed effects. The authors use a sample of 1,143 Chinese SOEs listed on the Shanghai and Shenzhen Stock Exchanges from 2004 to 2015.

The authors find that powerful dual role Party Secretaries are associated with greater bribery channel usage. Following the ongoing anticorruption campaign, SOEs with the powerful appointments significantly reduce their usage of both transparent (entertainment and travel costs) and opaque bribery (abnormal management expenses) channels. However, in general, Chinese SOEs respond to the anticorruption shock by switching from the more transparent to the opaquer bribery channel.

The authors contribute to the ongoing debate of politicians on corporate boards by examining the relatively unexplored area of government appointed top management and their influence on bribery at the firm level.

This article proposes a new approach to detecting the presence of common cyclical features when several time series are sampled at different frequencies. We generalize the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The first set is implied by identities stemming from the fact that the differences of the high-frequency I (1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed vector error-correction model (VECM) representations extend the results of Ghysels (2012) and are important for determining the correct set of variables to be used in a subsequent common cycle investigation. This fact has implications for the distribution of test statistics and for forecasting. Empirical analyses with quarterly real gross national product (GNP) and monthly industrial production indices for, respectively, the United States and Germany illustrate our new approach. We also conduct a Monte Carlo study which compares our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.