How do you search for the best alternative? Experimental evidence on search strategies to solve complex problems

Through a controlled two-stage experiment, we explore the performance of solution search strategies to resolve problems of varying complexity. We validate theoretical results that collaborative group structures may search more effectively in problems of low complexity, but are outperformed by nominal structures at higher complexity levels. We call into question the dominance of the nominal group technique. Further close examination of search strategies reveals important insights: the number of generated solutions, a typical proxy for good problem-solving performance, does not consistently drive performance benefits across different levels of problem complexity. The average distance of search steps, and the problem space coverage play also critical roles. Moreover, their effect is contingent on complexity:a wider variety of solutions is helpful only in complex problems. Overall, we caution management about the limitations of generic, albeit common rules-of-thumb such as "generate as many ideas as possible”

Different departments, different drivers

Purpose The purpose of this paper is to examine the antecedents and performance consequences of voluntary information exchange between the production and sales functions. Design/methodology/approach Building on the motivation-opportunity-ability framework, the authors first posit a general model for bilateral information exchange across functional levels. The innovation presented in this model consists in allowing both sides of such an exchange (e.g. production-to-sales and sales-to-production) to differ in the perceived adequacy of information they receive. The two sides can also differ in terms of how their motivation and ability impact that adequacy. To test the model, the authors make use of survey responses and objective data from sales, production and executive managers of 182 Chinese manufacturers. Findings Analysis of the sample shows that the sales-to-production exchange has a smaller estimated performance effect than the production-to-sales exchange. Although shared opportunity is important in predicting both sides of the exchange, the measure of motivation appears to only significantly impact the sales-to-production exchange. In contrast, the measure of ability only appears to significantly affect the production-to-sales exchange. Research limitations/implications Although limited to a regional context, differences in information-sharing drivers on the two sides of production-sales dyads pose strong implications that may be generalizable. Practical implications Specifically, these findings suggest alternative approaches and foci for resource investment that higher level managers can leverage in developing more effective cross-functional work settings. Originality/value This study differentiates itself from extant literature on information sharing by focusing on cross-functional (vs intra-functional) and voluntary (vs routine) information exchange

Assessing the Relative Performance of Nurses Using Data Envelopment Analysis Matrix (DEAM)

Assessing employee performance is one of the most important issue in healthcare management services. Because of their direct relationship with patients, nurses are also the most influential hospital staff who play a vital role in providing healthcare services. In this paper, a novel Data Envelopment Analysis Matrix (DEAM) approach is proposed for assessing the performance of nurses based on relative efficiency. The proposed model consists of five input variables (including type of employment, work experience, training hours, working hours and overtime hours) and eight output variables (the outputs are amount of hours each nurse spend on each of the eight activities including documentation, medical instructions, wound care and patient drainage, laboratory sampling, assessment and control care, follow-up and counseling and para-clinical measures, attendance during visiting and discharge suction) have been tested on 30 nurses from the heart department of a hospital in Iran. After determining the relative efficiency of each nurse based on the DEA model, the nurses’ performance were evaluated in a DEAM format. As results the nurses were divided into four groups; superstars, potential stars, those who are needed to be trained effectively and question marks. Finally, based on the proposed approach, we have drawn some recommendations to policy makers in order to improve and maintain the performance of each of these groups. The proposed approach provides a practical framework for hospital managers so that they can assess the relative efficiency of nurses, plan and take steps to improve the quality of healthcare delivery

Facilitating Organisational Fluidity with Computational Social Matching

Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Peer reviewe

Customer Experience Management

Dieser Beitrag leistet einen Beitrag zur Marketingforschung, da er den jungen aber von zunehmender Relevanz geprägten Forschungsstrang zum Themenkomplex CEM grundlegend entwickelt. Zum einen zeigt das identifizierte Rahmenkonzept auf, dass CEM über einzelne unternehmerische Fähigkeiten wie dem Design von Serviceerlebnissen, das die bisherige CEM-Forschung bestimmt hat, hinausgeht. Zum anderen leistet das Konzept einen Beitrag zur Synthese fragmentierter, aber miteinander zusammenhängender Literaturströmungen in der Marketingforschung ..