Behind the Scenes of Digitization: on the role of Sales employees' psychological needs inherent in usage of advanced digital technologies

Christina Henke, Sascha Alavi and Matthias Weiß

Digitization is transforming today’s business life, and firms continuously introduce new advanced technologies to manage customer information, coordinate decisions, and simplify communication processes (Hunter and Perreault 2007). Even though the average return on investment in technology is positive (World Economic Forum 2018), almost half of companies investing in digitization initiatives struggle to cover their expenses by increased productivity or performance (McKinsey&Company 2017). Against this background, the question rises as to what causes these differences and under which circumstances technology investments are successful.

Method and Data
In contrast to established technologies like email or notebooks, by advanced digital technologies, we refer to recently upcoming technologies in the areas of data collection, analytics, and communication. More precisely, we consider three types of advanced digital technologies, i.e., technologies (1) to collect and access data, e.g., cloud services, (2) to support decision making, e.g., predictive analytics applications, and (3) to communicate, e.g., video conferencing services (Hunter and Perreault 2007).
Drawing on self-determination theory, we suggest that frequent use of advanced digital technologies changes fundamental aspects of the working environment and, with this, the level of satisfaction of employees’ basic psychological needs for (1) autonomy, (2) competence, and (3) relatedness (Ryan and Deci 2000). More specifically, depending on the type of technology, usage intensity can either enhance or reduce need satisfaction. Furthermore, we assume that the perceived levels of autonomy and relatedness enhance work efficiency while perceived competence raises work effectiveness. We test our conceptual framework combining data from a baseline and an experience sampling survey.

Summary of Findings
Analyzing objective company data, we find that usage intensity of advanced digital technologies has an inverted u-shaped effect on job performance. Extending these findings, results of our second study show that, when employees intensely use technologies to collect and access data, this has positive effects on their perceived levels of autonomy and competence, but an inverted u-shaped effect on perceived relatedness. Moreover, intensely using technologies to support decision making has an inverted u-shaped effect on perceived autonomy and competence. Employees who heavily rely on technologies for communication purposes experience higher levels of autonomy and relatedness. In addition to this, the perceived levels of autonomy and relatedness have positive impacts on work efficiency, and competence positively affects work effectiveness.

Key Contributions
Our research contributes to existing research on usage of advanced digital technologies. In particular, we shed light on the reasons why some companies succeed and others fail when using new technologies to improve work processes. Unlike previous research, by considering not only antecedents to technology usage, but also its consequences for employee psychology, we show that the effect of technology usage intensity on employee job performance depends on the extent to which technologies help satisfy employees’ basic psychological needs. More precisely, we find that the level of need satisfaction explains 39% of the variation in work effectiveness and 31% of the variation in work efficiency. For companies, our results suggest that, if their aim of technology implementation is to raise work effectiveness, they should introduce technologies to collect and access data. In contrast, if they seek to enhance work efficiency, they may implement technologies which facilitate communication with colleagues or customers.

References
Hunter, Gary K. and William D. Perreault Jr. (2007), “Making Sales Technology Effective,” Journal of Marketing, 71 (1), 16–34.
McKinsey & Company (2017), “Digital McKinsey: Insights – Reinvention through Digital,” available at: https://mck.co/2xt3s0n [last accessed: November 28, 2019].
Ryan, Richard M. and Edward L. Deci (2000), “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being,” American Psychologist, 55 (1), 68–78.
World Economic Forum (2018), “The Future of Jobs Report,” available at: http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf [last accessed: November 28, 2019].

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Enhancing Salesforce Productivity in the Pipeline: The Promise of AI

Richard E Plank

 

Practitioners commonly use the term pipeline to describe their sales process. CRM programs such as Salesforce, (https://www.salesforce.com/) are used to monitor progress in individual sales efforts throughout an organization. Additional programs such as SuMO Motivate (https://www.cloudapps.com/) help companies better use CRM data, by defining what is needed, providing motivational options to get the data collected, and providing an improved platform to analyze findings

A next step is the use of artificial intelligence methods, which can be applied to CRM data to provide continuous learning based on new data provided. The technology is commonly referred to as Deep Learning (Liu, Lee, and Srinivasas 2019). An application is currently being developed by Cloudapps called Sensei. That application incorporates three different learning algorithms, including one that incorporates time into the analysis by including when a specific behavior is done within the sequence of actions reported in the pipeline. Such an application promises to provide what can be referred to a live and continuously updated best practices (Siguaw and Enz 1999). Incorporating this methodology will likely offer a significant competitive advantage to firms adopting it by providing better usage of sales resources.

The next step is to incorporate other types of data into the modeling in such a way as to improve the purely CRM data approach. The CRM approach can be referred to as a sales-centric approach as data is from the CRM program as provided primarily by salespeople. Two additional areas are suggested.

Psychological testing and specifically personality tests are often used in the hiring process to select employees, including salespeople (Scroggins, Thomas, and Morris 2009). More recently, the measurement of what is referred to as soft skills or non-cognitive factors has advanced, and a more inclusive test instrument now exists (https://www.intrinsicinstitute.com/).

Many organizations regularly test upon entry to the firm. The advantage of the newer test is that it measures factors that are not static but can not only change, but firms can use various techniques to foster that change. Thus, regular measurements and activities to improve these factors can add to the overall modeling of the sales pipeline and its improvement.

It has also been found that executing a sales behavior, while predictive and explanatory, does not have the impact that measuring the quality of that behavior has (Reid, Plank, and Minton 1997). While those differences may be reduced when time and order of behavior are considered, it is still likely that measuring quality will provide significant improvements.

The theoretical perspective is that of gaining work experience (Tesluk and Jacobs 1998). Disdar and Essen (2016) note that the desired outcome of work experience can be viewed as their ability in sensemaking. What AI will do in this situation is to document the various aspects of sensemaking as gained by the whole, and all salespeople can benefit from this.

Several research propositions will be presented, and methods of testing them will be briefly described.

 

References

Disdar, O.A., and A. Essen (2016), “Sensemaking at work: meaningful work experience for individuals and organizations," International Journal of Organizational Analysis, 24 (1), 2-17.
Liu, X., Lee, D., and K. Srinivasas (2019), “Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning,” Journal of Marketing Research 56 (6), 918-943.
Reid, D.A., R.E. Plank, and A.P. Minton 1997 "Behaviors and Sales Performance: What Do Buyers Respond to in Sales Presentations," Marketing Management Journal, 7 (1), 1-13.
Scoggins, W.A., Thomas, S.A., and J.A. Morris (2009), “Psychological Testing in Personnel Selection, Part III: The Resurgence of Personality Testing,” Public Personnel Management 38 (1), 67-77.
Siguaw, J.A., and C.A. Enz (1999), “Best Practices in Marketing,” Cornell Hotel and Restaurant Management Quarterly, 40 (5), 31-43.
Tesluk, P.E. and R.R. Jacobs (1998), "Toward an Integrated Model of Work Experience," Personnel Psychology 51 (2), 321-355.

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The use of social media by the sales force – is this a top down or bottom up driven
activity?

Kenneth Le Meunier-FitzHugh and
Leslie Caroline Le Meunier-FitzHugh


Using social media as a selling tool is becoming the norm for most selling
organisations and its pervasiveness in the selling process presents B2B sales teams with new
opportunities and challenges, which are hard to ignore (Ancillai et al., 2019). Facilitating
customer-focused selling and learning through social media is being seen as a management
issue (Moncrief et al., 2015). To generate value, management needs to be fully committed to
adopting sales technology in all its forms (Ogilvie et al., 2018). However, as ‘social selling’
frequently occurs within the salespeople’s individual social media domains, which are
individually generated and managed, the question is – Social media selling, is it driven from
the top down or from the bottom up?

Management should encourage salespeople to manage their customer relationships by
integrating information from automated systems (including CRM) with engagement on social
networks (Mariadoss et al., 2013). The belief is that social media selling can be achieved
through managerially led investment in new technologies and formal training programmes
(Limbu et al., 2014; Ogilvie et al., 2018), but this is not always the case. Organisations are
now hiring new salespeople partially based on their social footprint and their ability to
manage a variety social media sites, as well as traditional selling skills (Le Meunier-FitzHugh
and Douglas, 2016). Using their enhanced customer knowledge from social media
interactions, the theory is that it should be possible for the salesperson to build a picture of
what the customer values, and develop a relationship with them through seemingly informal
exchanges on various social media sites such as Twitter, Facebook, LinkedIn, as well as
blogs and vlogs (Nunan et al., 2018; Carter, 2014).

However, our initial research with salespeople from the GenY (Millennials, born
1980-94) and GenZ (born 1995-2015) is revealing that the development and adoption of
social media selling is being driven by salespeople, and not by management. Younger
salespeople are using social media interactions in their own way and aligned to their own
needs, to drive their sales relationships and their on-line activities in a much more integrated
and influential way than many managers realise. The interviewees explained that although
they were encouraged to use sales automated systems, they independently managed their
social media activities to keep their clients/customers engaged with their offers and that their
managers were largely unaware of their online activities. One stated “My manager was really
confused as he thought that I was not visiting my clients frequently enough, but my sales were
increasing. I had to explain that most of my interactions were now mostly through social
media”.

Technology-enabled salespeople are networking through social media with customers,
suppliers and even competitors within the industry, and so go beyond the traditional
boundaries of the buyer/seller relationship (Ancillai et al., 2019; Limbu et al., 2014). As a
consequence, managers should be aware of how social media is developing and is affecting
their sales operations. Managers need to understand the role that they play in becoming
aligned with how salespeople are interacting with their customers.

References
Ancillai, C., Terho, H., Cardinali, S. and Pascucci, F. (2019) Advancing social media driven
sales research: Establishing conceptual foundations for B-to-B social selling, Industrial
Marketing Management, 82 (10), 293-308.
Carter, B. (2014) “Three Steps to Build a Social Media Marketing Sales Funnel”,
http://www.socialmediaexaminer.com/social-marketing-sales-funnel/ <Accessed 14th August
2015>.
Le Meunier-FitzHugh, K. and Douglas, T. (2016) Achieving a Strategic Sales Focus, Oxford,
Oxford University Press.
Limbu, Y. B., Jayachandran, C. and Babin, B. J. (2014). Does information and
communication technology improve job satisfaction? The moderating role of sales
technology orientation. Industrial Marketing Management, 43(7), 1236-1245.
Mariadoss, B. J., Milewicz, C., Lee, S. and Sahaym, A. (2014). Salesperson competitive
intelligence and performance: The role of product knowledge and sales force automation
usage. Industrial Marketing Management, 43(1), 136-145.
Moncrief, W. C., Marshall, G. W. and Rudd, J. M. (2015). Social media and related
technology: drivers of change in managing the contemporary sales force. Business Horizons,
58 (1), 45-55.
Nunan, D., Sibai, O., Schivinski, B. and Christodoulides, G. (2018) Reflections of “social
media: Influencing customer satisfaction in B2B sales” and a research agenda, Industrial
Marketing Management, 85(11), 31-36.
Ogilvie, J., Agnihotri, R., Rapp, A. and Trainor, K. (2018) Social media technology use and
salesperson performance: A two study examination of the role of salesperson behaviors,
characteristics, and training, Industrial Marketing Management, 75(11), 55-65.

Author Contact:

Christina Henke
Sales Management Department,
Ruhr-University Bochum, Germany
christina.henke-k7j@rub.de
corresponding author

Sascha Alavi
Sales Management Department,
Ruhr-University Bochum, Germany
email: sascha.alavi@rub.de

Matthias Weiß
Center for Entrepreneurship, Innovation, and Transformation,
Ruhr-University Bochum, Germany
email: matthias.m.weiss@rub.de

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Richard E. Plank, PhD
Professor of Marketing
Muma College of Business
University of South Florida
4202 E. Fowler Av
Tampa FL 33620, USA
Rplank@usf.edu

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Kenneth Le Meunier-FitzHugh and
Leslie Caroline Le Meunier-FitzHugh
Norwich Business School
University of East Anglia
Norwich, UK
k.le-meunier-fitzhugh@uea.ac.uk
l.fitzhugh@uea.ac.uk