Moving beyond the direct effect of SFA adoption on salesperson performance: training and support as key moderating factors

To our knowledge, this research is the first to focus on the critical moderating role that user training and support play on the relationship between the use of sales force automation tools and salesperson performance (i.e., effectiveness: percent-to-quota and efficiency: average number of calls per day). Hypotheses are tested using survey data from a sample of 108 salespeople in a business-to-business context and archival sales performance information. Moderated regression analysis results indicate that the use of sales force automation tools only enhances salesperson efficiency and effectiveness under conditions of adequate user support and training. In fact, under low levels of user training and support, the use of sales force automation tools was found to reduce salesperson efficiency and effectiveness. These findings have important implications for IT and sales managers since the results show that only under certain conditions will companies realize a return on their investment in SFA tools. Limitations and future directions for research are then discussed.     

Management earnings forecasts and other forward-looking statements

We identify forward-looking statements (FLS) in firms’ disclosures to distinguish between “forecast-like” (quantitative statements about earnings) and “other”, or non-forecast-like, FLS.  We show that, like earnings forecasts, other FLS generate significant investor and analyst responses. Unlike earnings forecasts, other FLS are issued more frequently when uncertainty is higher. We then show that earnings-related FLS are more sensitive to uncertainty than quantitative statements, suggesting that managers are more likely to alter the content than the form of FLS when uncertainty is higher.  Our study indicates that incorporating other FLS into empirical measures provides a more comprehensive proxy for firms’ voluntary disclosures.     

POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.     

Seemingly inconsistent analyst revisions

In about 20%–30% of cases where an analyst revises two outputs (namely, earnings estimates, target prices, or stock recommendations) simultaneously, the two estimates are revised in opposite directions. Existing literature notes that these inconsistent outputs are widespread, and concludes that they are lower-quality, driven by strategic bias, and are viewed as less valid by investors. We find that these characterizations are generally inaccurate. Apparent inconsistency is largely driven by accounting and economic factors, with only limited evidence that investment banking-related conflicts play a role. Moreover, inconsistent outputs are neither less accurate than consistent outputs nor do they resolve less investor uncertainty upon their release. Overall, our results suggest that researchers should be cautious in interpreting the correlation between analyst outputs as a measure of bias or quality, and in using a single analyst output as a proxy for an analyst's overall views.     

Does Human Capital Protect Workers against Exogenous Shocks? Evidence from Panel Data on South Africa during the 2008‐2009 Crisis

The financial and economic crisis of 2008 and 2009 took a heavy toll on the South African economy. The economy contracted for the first time since 1998 and entered recession during the fourth quarter of 2008. The gross domestic product contraction was soon transmitted to the labour market. Between the second quarters of 2008 and 2009, employment fell by 3.8%. However, not all individuals were hit with the same intensity. Using panel data from a quarterly labour force survey unique in the African context, we find that human capital (i.e. education as years of schooling and workforce experience) provided a buffer against the shock. After controlling for observable characteristics, education and experience showed the potential to entirely offset the effect of the recession on the likelihood of employment. This has important policy implications, as it strengthens the case for strategic investments in human capital and helps identify the unskilled as having the greatest need for social safety net interventions during a recession.     

Geographic distribution of suicide and railway suicide in Belgium, 2008–2013: a principal component analysis

This study investigated the geographic distribution of suicide and railway suicide in Belgium over 2008--2013 on local (i.e., district or arrondissement) level. There were differences in the regional distribution of suicide and railway suicides in Belgium over the study period. Principal component analysis identified three groups of correlations among population variables and socio-economic indicators, such as population density, unemployment, and age group distribution, on two components that helped explaining the variance of railway suicide at a local (arrondissement) level. This information is of particular importance to prevent suicides in high-risk areas on the Belgian railway network.     

Collective self-governance in a marine community: expedition cruise tourism at Svalbard

Collective self-governance is gaining attention in the literature for maintaining the quality of key attractions and promoting sustainable tourism. The long-term success of collective self-governance is dependent on both its internal organization and its embeddedness in external state and non-state regulations. This paper presents the marine community concept, consisting of a policy and a user community, as a framework for investigating the internal and external dynamics of collective self-governance and its ability to steer toward sustainable cruise tourism. As methodology, a case study design was chosen which was primarily studied by means of interviews with a spectrum of relevant actors concerning expedition cruise tourism at Svalbard. By applying the marine community to Svalbard expedition cruise tourism governance, we draw the following conclusions: (1) collective self-governance complements governmental regulation through access to knowledge, conflict resolution and rule-compliance based on disclosure, traceability and trust; (2) collective self-governance's increasing role in the policy community alienates the expedition crew from the user community; and (3) informational overflow by co-existence of collective self-governance and state-governance challenges sustainable cruise tourism. Collective self-governance would, therefore, benefit from reflection, especially regarding the role of the user community that functions as an intermediary between state and self-governance regulations.     

Modeling farmers’ decisions on tea varieties in Vietnam: a multinomial logit analysis

This article analyzes households’ choice on tea varieties in Vietnam by using a multinomial logit model. The modeling takes into account the issue of unobserved individual heterogeneity and the endogeneity of some explanatory variables (use of chemical and organic fertilizers). The results show that important factors influencing the decision to adopt one type of tea varieties include income, age, household size, farming contract, and use of organic fertilizers, but also membership of professional associations such as the Tea Association and the Farmers Union.