Data Collection

Data collection for studying the research model introduced in Chapter 4 involves gathering second-hand data in the sports car segment of the automotive industry. The

automotive industry possesses several characteristics that are particularly desirable for this study.

The first characteristic is the widespread use of IOS in the automotive industry. The

automotive industry was among the earliest industries to adopt IOS, such as EDI systems for purchasing, inventory management, and production scheduling (Cash and Konsynski 1985). Some major automakers (such as the Big-Three) are also aggressive IOS users. They are trying to digitize their core business processes and link suppliers, dealers, logistics parties, and

customers on common computing platforms. This aggressive use of IOS has spurred many IOS innovations.

The second characteristic is the proliferation of “co-optition” in the automotive industry.

Many automakers compete and collaborate at the same time for reducing costs and sharing risks. Collaboration spans a wide range of dimensions, including procurement, product development, production, logistics, marketing & sales, and service.

The third characteristic is the unique setting of buyer-seller relations in the automotive

industry. In the automotive industry, federal laws stipulate that automakers cannot sell cars

directly to individual buyers. It is thus difficult for automakers to reach and collect information directly from individual customers via conventional means (e.g., sales phone calls). IOS may overcome this consequence of federal regulation by enabling the automakers to establish direct links with individual customers at a low cost, thus changing buyer-seller relations in the

automotive industry.

These three characteristics of the automotive industry provide a favorable research background and high quality data for examining IOS’s roles and competitive dynamics in an e- business context.

Choosing the sports car segment has two major reasons. First, focusing on one car segment can exclude confounding factors related to different industry/segment characteristics. Second, sports cars, defined as small low vehicles with a high-powered engine that usually seats two persons (www.wordreference.com), are distinct from the other vehicles like sedans, SUVs, and wagons, and easy to identify. This distinction between sports cars and other vehicles can remove ambiguities in identifying major automakers, their relevant competitors, competitive actions and system usages during the data collection.

Based on the SIC code of sports car segment (3711125), nine major sports car makers have been identified as feasible for the second-hand data collection. The nine automakers are BMW AG, DaimlerChrysler AG, Ford Motor Co., General Motor Corp., Mazda Motor Corp., Mitsubishi Motors Corp., Nissan Motor Co., Toyota Motor Corp., and Volkswagen AG. Second-hand data include automakers’ collaborative relationships, their IOS use, and

competitive actions. Automakers’ IOS use is actual and voluntary. Actual use refers to the

manner in which IOS are implemented and in effect used, rather than intended use (DeLone and

McLean 2003) or self-reported use (Devaraj and Kohli 2003). Voluntary use refers to that the

adoption of system is non-mandatory (DeLone and McLean 2003; Devaraj and Kohli 2003). Data sources include SDC database, COMPUSTAT, F&S Predicast’s Index, thousands of articles from over dozens of major trade publications, as well as miscellaneous Web sites. Table 5.1 lists these data sources.

This study is unique in its collection of actual, voluntary IOS use data from second-hand

data sources like news reports and trade articles. Second-hand data sources are widely used in social network analysis and competitive dynamics research for collecting data about

collaborative relationships and competitive actions. Prior IOS empirical research generally collects self-reported data. Self-reported data involve asking the same respondents to answer questions on their perceptions of system use and effectiveness (Devaraj and Kohli 2003). Self- reported usage measures can provide an important indicator in assessing IS success or

effectiveness. But these measures have several limitations. (1) Self-reported usage might induce biases due to obtaining information from a single source or a same respondent, known as

common method variance. In this regard, second-hand data collection increases data reliability by identifying data from multiple information sources. (2) Some studies have suggested that perceived system usage may not be congruent with actual usage (Straub et al. 1995), and thus

might not be an appropriate surrogate for actual usage (Szajna 1996). Possible explanations for the discrepancy between actual usage and perceived usage are subjects’ difficulty in recalling their past usage, exaggeration of the extent of usage to fit in with their superiors’ expectations, attention lapses, and bounded rationality (Devaraj and Kohli 2003). As such, there is an

increasing recognition that actual system usage provides better measures than self-reported usage in assessing IS performance impacts (DeLone and McLean 2003; Devaraj and Kohli 2003). (3) Second-hand data sources (like news reports and trade articles) allow data to be collected in a relatively controlled manner, especially when collecting longitudinal data or sensitive data (like collaborative relationships, competitive actions, and significant system implementation and usage), which are generally difficult to obtain in a self-reported manner.

Table 5.1 Data sources used in this study

DATABASE, INDEX, &

WEB SITE TRADE PUBLICATION

COMPUSTAT Database Advertising Age ADWEEK New

England Edition Aftermarket Business

American Metal Market SDC Database Arizona Business

Gazette Arkansas Business

Automotive Design & Production

Automotive Industries F&S Predicast’s Index Automotive News Autoparts Report AutoWeek Barron's Autoweb.com (Sports

Car Center) BC Business Best's Review Boston Globe (MA) Campaign Bmw.com Chemical Week Chicago Sun Times

(IL) Computerworld

Crain's Chicago Business Computerworld.com Crain's Cleveland

Business

Crain's Detroit

Business Daily News Record Diesel Fuel News Daimlerchrysler.com

Diesel Progress North American Edition

Electronic

Engineering Times Entrepreneur

European Rubber Journal

Ford.com Far Eastern _{Economic Review} Fleet Owner Florida Trend Globe & Mail _{(Toronto, Canada)}

Gm.com

HFN The Weekly Newspaper for the Home Furnishing Network

Indonesian Commercial Newsletter

Inzhenernaia Gazeta Los Angeles Business Journal

Mitsubishi-motors.com Machine Design Marketing Week

National Underwriter Property & Casualty- Risk & Benefits Management

New Scientist

Nissan-global.com Plastics News Precision

Marketing Promo Purchasing

Pressroom.toyota.com Research Alert Rubber & Plastics

News The Middle East

The New York Times Vw.com The Oil Daily The Wall Street

Journal Tire Business Tire Review Yahoo.com (Auto

Section)

U.S. News & World

Report USA Today

Vietnam Investment Review

Wall Street Journal. Europe

Ward's Auto World Ward's Automotive