Short Biography of Torrance Mayberry

Thursday, June 2, 2016

Deciphering how the newfound connective social traces of people are reframing modern eCommerce. (Aug 2015)

The Present Condition:

Many species, including people, are social organisms that leave behind social traces in nature and society (Freeman, 1995 and Baars, 1997).  The social traces people leave behind are natural occurring phenomenon and people generate these to represent their existence.  Given the right conditions for an online ecosystem people generate social traces that coalesce to form newfound connective social traces of online behaviours.  Unlike early research that saw the technology as separate from the evolutionary behaviours of people.   These newfound connective social traces of people are inseparable from their evolutionary behaviours and they create value for modern eCommerce.   Also, the impacts of these newfound connective social traces of people has been overlooked for modern eCommerce and are especially relevant in the rising big data technology and the internet of things (that is, the sensory of things in society).  In deciphering newfound connective social traces recorded in free social applications like Yelp, the traces reveal early stage decision making behaviours of conscious choices people act on while trading for necessities through eCommerce and provide merchants with summary consumer data (King, 2011).  These newfound connective social traces reveal the early stage decision making behaviours in people whenever their trading decisions are intentional (James, 1890).  The decision making trading behaviours of people represent conscious or voluntary choices (that is, psychological) they act on when using social applications (Baars, 1997).    In deciphering the newfound connective social traces for the brains neural circuitry (that is, physiological), the neural circuitry also reveals early stage decision making behaviours for people’s social motives (that is, fulfilling the need to be social) whenever they interact with social applications like Facebook.  Innately, the brain’s neural circuitry has permanent connections that stimulate behaviours people act on to be social with others, even if individuals are presently in non-social moments (Leberman, 2015).  

In line with suggestions by James (1890), Baars (1997), Lieberman (2015), some research deciphers the newfound connective social traces in order to create frictionless technology to reduce stressors that people can experience while using technology.   Stressors like technostress (that is, stressors people experience while interacting with technology) affect the productivity of people (Tams et al., 2014).  In deciphering newfound connective social traces in social applications like Twitter, stressors experienced by merchants while interacting with a financial services unreliable payments technology reveal emotional behaviours of frustration.  Also, newfound connective social traces reveal merchants were conscious of the effects of unreliable technology on income loss and productivity loss (The Sydney Morning Herald, 2015).  This form of newfound connective social traces indicates the effects of technostress flow beyond traditional boundaries of organisations’ end users.  There is evidence (Davis et al., 2011; Tams et al., 2014; Silva et al., 2014), that combining newfound connective social traces for people’s decision making behaviours with previously existing information sources, can reveal new discoveries for eCommerce, technostress and the internet of things (IoT).   Organisations unable to move beyond deciphering previously existing transactional information face problems in effectively making decisions to grow their businesses in the 21st Century.
The Practical Problem:    

Most organisations face a practical problem in deciphering how the newfound connective social traces of people are reframing modern eCommerce.  For instance, organisations including financial services firms have been collecting, processing and aggregating business information on their customers for years.  However, despite collecting enormous amounts of customer information, the data sets collected provide no insights to decipher correctly each stage of people’s decision making behaviours, including their early stages.  At present, the interplay between the newfound connective social traces and the early stages of people’s decision making behaviours that they act on with merchants online, is ahead in time of the decision making ability of financial services firms.   The inability of financial services firms to decipher the early stages of people’s decision making behaviours collected in social applications creates a perpetual time lag.  This time lag makes predicting demand or determining what the consequences of different actions may be for merchants unlikely (Simon, 1955). This limited use of available information within financial service firms hamper their ability to understand the decision making behaviours of people for the benefit of merchants, in the 21st Century.  There is a need for combining dimensions of the entire person’s decision making behaviours and social patterns that are reframing modern eCommerce. 

The Research Questions:

This research will attempt to decipher how the natural occurring phenomenon humans generate for the newfound connective social traces, are reframing modern eCommerce.   The research intends to explain the interplay between causal mechanisms for the early and final stages of decision making behaviours reframing eCommerce, Technostress and the IoT.   An important series of articles (Shmueli, 2010; Breiman, 2001; Breiman et al., 1984) suggest conducting statistical modelling as one way to explore and develop hypotheses, which can be tested by targeted experiments.  Whilst recognising their complexity, both explanatory and predictive modelling dimensions will combine both the newfound connective social traces and the final interactions people have with merchants at the end of the decision making process for settlements of payments of goods.  The dimensions will be used for testing the following hypotheses:

  •  Are decisions associated with merchants’ expansion goals correlated to the predictability power in market characteristics modelling?
  • Will the model with the most explanatory or predictive power enable both relationship managers and merchants to improve their judgement when choosing between patterns for growth markets?
  •  Do these models relate to patterns or behaviour and decision making to enable increased growth/profit for merchants and relationship managers?
  •  What stressors associated with unreliable banking technology affects the emotions of merchants or other people in society?
  •   What newfound connective social traces of people are becoming networked into the IoT? 
How I hope To Conduct the Research: 

This study aims to use a mixed method approach (that is, both qualitative and quantitative) to explore the research hypothesis/questions.  A mixed method approach was selected for conducting this study because the approach interweaves the best of both qualitative and quantitative methods into a lone study (Creswell, 2003 and Bryman, 2007).  Consequently, this study intends to integrate these methods into a lone study that will cover three stages.  The first stage will be quantitative, and will cover a period of one to two years.  This first phase intends to collect quantitative data to analyse the data and to identify taxonomies.   In this first phase an instrument (that is, learning algorithms) will be created based on those identified taxonomies to conduct measurements on a large sample in order to reveal how the newfound connective social traces of people are reframing modern eCommerce.  The second phase is qualitative, and will be carried out in parallel with phase one.  Qualitative data will be collected and analysed using response codes to identify advice categories.  In this second phase an instrument (that is, focused questions, transcripts) will be created from a randomised sample that will be conducted with relationship managers in order to reveal what advice categories are associated with the judgments of both merchants and relationship managers when choosing between patterns for growth in markets.   This second phase intends to identify the interchange between those advice outcomes that connect with merchant’s expansion goals or needs. The third stage for this study is an analysis, interpretation and discussion of the overall research findings.  It is intended that this third stage covers a period of one and a half years.  This third phase will explain those behaviours and capabilities for deciphering how the newfound connective social traces of people are reframing modern eCommerce. 

Newfound Connective Social Traces Are Everywhere (Mayberry, 2015)


1.  Baars, B. J. 1997. In the Theater of  Consciousness. Oxford: Oxford University Press.
2. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group. 
3.  Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by theauthor).  Statistical Science, 16(3), 199-231.  
4.  Bryman, A. (2007).  Barriers to integrating quantitative and qualitative research.  Journal of Mixed Methods Research, 1, 8-22.
5.  Creswell, J. W. (2003).  Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage Publications.
6.  Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). Research commentary-NeuroIS: the potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687702. 
7.  Freeman, W. J. 1995. Societies of Brains. Hillsdale NJ: Lawrence Erlbaum.
8.  James, W. 1890. The Principles of Psychology. Cambridge, MA: Harvard University Press. 
9.  King, G. (2011). Ensuring the data-rich future of the social sciences. Science, 331(6018):719–721. 
10.  Shmueli, G. (2010). To explain or to predict? Statistical Science, 289-310. 
11.  Simon, H. A. (1955). A behavioural model of rational choice. The quarterly journal of economics, 99-118.
12.  Spunt, R. P., Meyer, M. L., & Lieberman, M. D. (2015). The Default Mode of Human Brain Function Primes the Intentional Stance. Journal of cognitive neuroscience.
13.  Tams, S., Hill, K., Ortiz de Guinea, A., Thatcher, J., & Grover, V. (2014). NeuroIS—alternative or complement to existing methods? illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research. Journal of the Association for Information Systems, 15(10), 723-753. 
14.  The Sydney Morning Herald. (2015). Online retailers blame lost sales on NAB payment system problems. Retrieved June 09, 2015 from

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