Multi-channel-management bei premiummarken

Die Entwicklungen im Rahmen der Informations- und Kommunikationstechnologien haben die Ausbreitung des Multi-Channel-Managements (MCM)¹ vorangetrieben. Dabei werden die Kan?le jedoch h?ufig nicht koordiniert eingesetzt (vgl. Sch?gel/Sauer 2002, S. 26). Für das Markenmanagement kann dies negative Folgen haben, denn nur über eine konsistente und kontinuierliche Vermittlung der einzelnen Komponenten einer Marke² an allen Brand Touch Points kann eine stabile und langfristig tragf?hige Marke-Kunden-Beziehung aufgebaut werden (vgl. Burmann/Meffert 2005, S. 67). Im Folgenden sollen die Chancen und Risiken eines Multi-Channel-Managements bei Premiummarken genauer betrachtet werden.     

Combination of Tests for Cointegration in Cross‐Correlated Panels

In this paper, Cheng and Sheng's (2017) combination of ‘combinations of P‐values’ (CCP) is extended to a combination of more than two tests and applied for cointegration testing in cross‐correlated panels. In a Monte Carlo experiment, power and size of the different combinations of combinations are investigated. If uncertainty about the panel configuration is taken into account, the results indicate that a multi‐test combination can minimize power losses. Furthermore, the usefulness of the combinations studied is illustrated by an application to international interest rate linkage. Cross‐sectional dependencies in both the simulation and the empirical studies are accounted for by using the block bootstrap.     

Determinants of Smallholder Farmers' Marketing Channel Choice: Evidence from the Vietnamese Rice Sector

Cash crops such as specialty rice and other high‐value varieties produced for domestic and international markets are considered an increasing source of income for smallholder farmers in many Asian countries. The present study focuses on the factors affecting Vietnamese specialty rice farmers' choice of marketing channel and how their choice influences farm performance. The analysis has been conducted using multinomial logit and linear regression models on quantitative data collected from 280 specialty rice farmers in the Red River Delta, one of the main rice production regions in Vietnam. Results reveal that even though local collectors and wholesalers are still the most common recipients of farmers' goods in rural areas, reduction in transaction costs with regard to uncertainty influences farmers to choose modern marketing channels through collective action (via specialty rice farmer associations). This collective marketing channel helps farmers increase average prices received by US$0.028 per kg of paddy. Based on the results, manifold political implications are derived.     

The nexus of business sustainability and organizational learning: A systematic literature review to identify key learning principles for business transformation

Companies play a central role in the quest for sustainable development. Organizational learning theories have been utilized to explain sustainability-related change processes in firms. However, implications from studies at the nexus of business sustainability and organizational learning are highly dependent on varying conceptualizations. The objective of this study is to provide clarity on the plurality of conceptual underpinnings in research and to uncover principles that are associated with deeper organizational change processes, that is, business transformation. Building on insights from a systematic literature review, we develop a sustainability learning typology, from which we distill three learning principles for business transformation: (1) the deutero learning mode, (2) the societal learning scope, and (3) the cooperative advantage objective. We formulate needs for future research to further elaborate on the learning principles associated with business transformation and suggest implications for practice.     

Trade‐offs in the performance of alternative farming systems

Numerous alternative farming systems are proposed as solutions to the sustainability challenges of today's conventional farming systems. In this paper, we review the production, environmental, and socioeconomic performance of three widely discussed and promoted alternative farming systems—organic, smallholder, and urban agriculture. We show that both organic and smallholder agricultures have some benefits, but also entail important trade‐offs; organic has environmental benefits, and also livelihood, health, and nutritional benefits for producers and consumers, but is hampered by lower yields and higher prices. Smaller farms have higher yields and host higher biodiversity, but are hampered by lower incomes to farmers. Urban agriculture can take some pressure off rural landscapes, provide nutritional benefits to the urban poor, and engage urban dwellers in addressing food system challenges, but it simply cannot scale up to be a substantial solution in and of itself. We suggest that instead of focusing on alternative systems, we should identify pathways to sustainable farming for all systems, reforming conventional systems where they perform poorly, and transitioning to alternative systems in contexts where they perform best.     

The Recipe for the Perfect Review?

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.     

Does acquiring venture capital pay off for the funded firms? A meta-analysis on the relationship between venture capital investment and funded firm financial performance

Researchers and practitioners frequently propose that venture capital (VC) is an important resource to increase the performance of funded firms, especially in environments of uncertainty. In this paper we scrutinize these theoretical propositions, following an evidence-based research approach. We synthesize 76 empirical samples on 36,567 firms. We find a small positive performance effect of VC investment on funded firm performance; however, the effect vanishes if researchers control for industry selection effects. Furthermore, we find that the performance effect mainly relates to firm growth while profitability is unaffected. We also uncover that performance effects are reduced when the funded firms are very young or very mature. In addition, studies focusing on IPO events, which constitute the majority of studies, determine a substantially smaller performance effect. We discuss theoretical implications and offer suggestions for future research on VC.     

Optimal portfolio selection via conditional convex risk measures on L p

We consider conditional convex risk measures on L ^p and show their robust representation in a standard way. Such measures are used as evaluation functionals for optimal portfolio selection in a Black&Scholes setting. We study this problem focusing on the conditional Average Value at Risk and the conditional entropic risk measure and compare the respective optimizers.     

Evaluating rater training with double-pretest one-posttest designs: an analysis of testing effects and the moderating role of rater self-efficacy

Abstract

Previous research on performance appraisal in Human Resource Management has found that frame-of-reference (FOR) training can improve rating accuracy. However, both the time-consuming development of training materials and the almost exclusive use of experimental evaluation designs limit the dissemination of FOR training as well as a better understanding of how and when it works. Therefore, unlike past research, the present studies used more general rater training materials and examined improvements in rater accuracy by means of a double-pretest one-posttest design. Study 1, using a student sample (the majority being part-time employees; N = 58), demonstrated the effectiveness of the rater training over and above a testing effect. Study 2 with participants from the workforce population (N = 45) replicated these results. In addition, in Study 2, pre-training rater self-efficacy moderated rater training effectiveness such that the rating accuracy of trainees with low self-efficacy improved more than did accuracy scores of trainees high in rater self-efficacy. We conclude that an effective FOR training can be conducted with less organization-specific materials, though both testing effects must be controlled for and individual differences (i.e. rater self-efficacy) between trainees have to be taken into account.     

Economic Transition and Growth: A Replication

Phillips and Sul (Journal of Applied Econometrics 2009, 24 , 1153–1185) provide an algorithm to identify convergence clubs in a dynamic factor model of economic transition and growth. We provide a narrow replication of their key results, using the open source R software instead of the original GAUSS routines. We are able to exactly replicate their results on convergence clubs, corresponding point estimates and standard errors. We comment on minor differences between their reported results and their clustering algorithm. We propose simple adjustments of the original algorithm to make manual intervention unnecessary. The adjustments allow automated application of the algorithm to other data. Copyright © 2016 John Wiley & Sons, Ltd.