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The main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.

The paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.

Relative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.

This study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.

This study seeks to determine the effects of stay-at-home orders in Spring 2020 on COVID-19 cases and deaths in the Central USA by comparing counties and health service areas that were and that were not subject to statewide orders.

This study estimates the effects of statewide stay-at-home orders on new COVID-19 cases and deaths within 19 central states, of which 14 had stay-at-home orders. It uses synthetic control analysis and nearest neighbor matching to estimate the effects at two geographic levels: counties and health service areas.

Statewide stay-at-home orders significantly reduced the number of new COVID-19 cases in the Central USA starting about three weeks after their effective dates; during the fourth week after their effective dates, the orders reduced the number of new cases per capita by 31%–57%. Statewide stay-at-home orders did not reduce the number of new COVID-19 deaths in the Central USA.

The main purpose of stay-at-home orders in Spring 2020 was to “flatten the curve” so that hospitalizations would not exceed capacity. It is likely that stay-at-home orders in the Central USA reduced hospitalizations to some extent, although the effect on hospitalizations was likely smaller than the effect on cases.

This is the first study of stay-at-home orders in the USA to limit the population to a group of interior states. All coastal states had statewide stay-at-home orders and comparing coastal states with orders to interior states without them may be problematic.

Understanding customers is critical for service researchers and practitioners. Today, customers are increasingly active online, and valuable information about their opinions, experiences and behaviors can be retrieved from a variety of online platforms. Online customer information creates new opportunities to design personalized and high-quality service. This paper aims to review how netnography as a method can help service researchers and practitioners to better use such data.

A systematic review and analysis were conducted on 321 netnography studies published in marketing journals between 1997 and 2017.

The systematic review reveals that netnography has been applied in a variety of ways across different marketing fields and topics. Based on the analysis of existing netnography literature, empirical, theoretical and methodological recommendations for future netnographic service research are presented.

This paper shows how netnography can offer service researchers unprecedented opportunities to access naturalistic online data about customers and, hence, why it is an important method for future service research.

Netnographic research can help service firms with, for example, service innovation, advertising and environmental scanning. This paper provides guidelines for service managers who want to use netnography as a market research tool.

Netnography has seen limited use in service research despite many promising applications in this field. This paper is the first to encourage and support service researchers in their use of the method and aims to stimulate interesting future netnographic service research.

The idea to use computers for teaching and learning is over 50 years old. Numerous attempts to use computers for knowledge dissemination under a variety of names have failed in many cases, and have become successful in others. The essence of this paper can be summarized in two sentences. One, in some niches, applications tend to be successful. Second, attempts to fully eliminate humans from the educational process are bound to fail, yet if a large number of aspects is handled well, the role of teachers can indeed be much reduced. The paper aims to discuss these issues.

Report on experimental results.

In some niches, applications of e-Learning technology tend to be successful. However, attempts to fully eliminate humans from the educational process are bound to fail, yet if a large number of aspects is handled well, the role of teachers can indeed be much reduced.

A number of features that seemed essential in earlier e-Learning systems turn out to be superfluous.

New e-Learning systems have to concentrate on quality of content, not complex technology.

E-Learning the right way helps learners, teachers and institutions.

Experiments reported verify or do the opposite of often loosely stated opinions.