Rethinking Service Companies’ Business Logic: Do We Need a Customer-Dominant Logic as a Guideline?

Purpose –This paper explores and expands the roles of customers and companies in creating value by introducing a new a customer-based approach to service. The customer’s logic is examined as being the foundation of a customer-based marketing and business logic. Design/methodology/approach – The authors argue that both goods-dominant logics and service-dominant logics are provider-dominant. Contrasting the customer-dominant logic with provider-dominant logics, the paper examines the creation of service value from the perspectives of value-in-use, the customer’s own context, and the customer’s experience of service. Findings –Moving from a provider-dominant logic to a customer-dominant logic uncovered five major challenges to service marketers: Company involvement, company control in co-creation, visibility of value creation, locus of customer experience, and character of customer experience. Research limitations/implications – The paper is exploratory. It presents and discusses a conceptual model and suggests implications for research and practice. Practical implications –Awareness of the mechanisms of customer logic will provide businesses with new perspectives on the role of the company in their customer’s lives. We propose that understanding the customer’s logic should represent the starting-point for the marketer’s business logic. Originality/value – The paper increases the understanding of how the customer’s logic underpins the customer-dominant business logic. By exploring consequences of applying a customer-dominant logic, we suggest further directions for theoretical and empirical research.

Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit