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Learning Service Experience from the Service Encounter of a Retailing Chain Storefront

Classification
Dimension Value
  • Type of the Research Result
  • Empirical Study
    • Observation Results
  • Current Status of Development
  • In Progress
    • With Pilot Application
  • Number of Cases
  • 1
  • Functional Area
  • Core Processes
    • Core Processes First Level
      • Design and Implementation of Product-Service-Systems
    • Core Processes Second Level
      • Distribution of Product-Service-Systems
  • Company Size
  • Not Specified
  • Lifecycle Phase
  • Utilization
  • Types of Customers of Value Bundles
  • Private Customers
  • Industry Sector
  • Trade
  • Standardization
  • No Standardization

 

In  this  explorative  research,  the authors  seek  to  find  the most important service experience variables determining the customer purchase decision and the clerks’ influence on customers’ purchase. This study conducted  a  case  study  of  a  children’s  apparel company, denoted L, which has 243 retailing stores. Company  L  has  implemented  Point  of  Sale  (POS) systems  in  its  retailing  stores,  and  the  management wanted  to  know  what  functions  can  be  added  in facilitating   storefront   employees   achieve  better customer service experience. We, therefore, focus on observing    the    services provided    by    storefront employees and  their  reflection on customer’s purchasing decision  in  a  retailing  store.  The  study generated   decision   trees via  Waikato   Environment   for Knowledge Analysis (WEKA)   to analyze multiple data sources in order to (1) understand what makes good service experience in service encounter, (2)  get  explicit  knowledge  from  service  encounter information, and (3) learn about customers for clerks to internalize tacit knowledge. The findings can be used to add into company L’s POS system in order to guide  storefront  employees  to  learn  from  trained decision    rules. Moreover,  the company can internalize service experience  knowledge by aggregating learned rules from all retailing stores of the company. 

This research result was described by Sanja Tumbas (4. July 2011 - 18:57)
This research result was last edited by Sanja Tumbas (13. November 2011 - 16:21)

Further information


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