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This paper aims to investigate how a small business is able to continually use entrepreneurial financial sources (i.e. crowdfunding) within and after a successful transformation from an entrepreneur. It additionally investigates how a market incumbent is able to successfully join the market of entrepreneurial financial resources.

Therefore, a comparative case study using qualitative and quantitative data as well as triangulation technique is conducted within the international board game (or tabletop) market at the crowdfunding platform Kickstarter. The US company CMON, which has developed from an entrepreneur to a small business and beyond, is compared with the German incumbent Pegasus. Based on an analysis of a set of key performance indicators suggested in the literature, qualitative and quantitative variables are deductively derived to measure their impact on the financial goal achievement, thereby showing their impact on the goal achievement. During the analysis, additional variables are identified inductively.

As a result, several qualitative components are found to be crucial, including oral storytelling and computer animated videos/images, a perfect multilingual product language, prototyped components, an active community and a depth and regularity in campaign updates. In quantitative terms, important components include having more product images than longer project descriptions, more optional buys than different but fixed project rewards, a big social network (on Twitter and Facebook), and the number of updates.

Based upon the data and findings, this study invites for more research, especially in conducting a larger scale quantitative analysis using the developed framework to compare more cases within a branch, cases across branches and cases with different background stories.

But to successfully run a crowdfunding campaign, entrepreneurs and incumbents can use the provided measures as a first design- and decision-roadmap, as well as copying the new business strategy of continually practicing crowdfunding for new products.

Despite its limits, this paper offers the first in-depth qualitative and quantitative crowdfunding case study showing on the one hand a new business strategy about crowdfunding as well as providing a structured measure to compare crowdfunding project performance.

Within the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.

The study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.

This study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.

This study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.

This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R²/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered.

The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006).

The analysis reveals a substantial artificial neural network model performance (R² > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.

Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.